EDA Profiling Report

Dataset statistics

Number of variables	14
Number of observations	45556
Missing cells	177031
Missing cells (%)	27.8%
Duplicate rows	158
Duplicate rows (%)	0.3%
Total size in memory	5.2 MiB
Average record size in memory	120.0 B

Variable types

Unsupported	1
Text	12
Categorical	1

Alerts

Dataset has 158 (0.3%) duplicate rows	Duplicates
`source_id` has 38711 (85.0%) missing values	Missing
`author` has 7004 (15.4%) missing values	Missing
`url_to_image` has 2826 (6.2%) missing values	Missing
`full_content` has 42986 (94.4%) missing values	Missing
`article` has 42584 (93.5%) missing values	Missing
`title_sentiment` has 42584 (93.5%) missing values	Missing
`article_id` is an unsupported type, check if it needs cleaning or further analysis	Unsupported

Reproduction

Analysis started	2024-02-15 15:56:38.905856
Analysis finished	2024-02-15 15:57:40.685357
Duration	1 minute and 1.78 second
Software version	ydata-profiling vv4.6.4
Download configuration	config.json

article_id
Unsupported

REJECTED UNSUPPORTED

Missing	0
Missing (%)	0.0%
Memory size	711.8 KiB

source_id
Text

MISSING

Distinct	86
Distinct (%)	1.3%
Missing	38711
Missing (%)	85.0%
Memory size	711.8 KiB

Length

Max length	36
Median length	25
Mean length	11.048064
Min length	2

Characters and Unicode

Total characters	75624
Distinct characters	45
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	17 ?
Unique (%)	0.2%

Sample

1st row	al-jazeera-english
2nd row	abc-news-au
3rd row	bbc-news
4th row	the-times-of-india
5th row	the-jerusalem-post

Value	Count	Frequency (%)
the-times-of-india	1126	16.4%
bbc-news	720	10.5%
al-jazeera-english	555	8.1%
rt	350	5.1%
abc-news	339	5.0%
business-insider	330	4.8%
newsweek	275	4.0%
abc-news-au	193	2.8%
time	179	2.6%
espn	178	2.6%
Other values (77)	2602	38.0%

Most occurring characters

Value	Count	Frequency (%)
e	10086	13.3%
-	8419	11.1%
s	6751	8.9%
i	6490	8.6%
n	5932	7.8%
a	4920	6.5%
t	4641	6.1%
b	3126	4.1%
r	2908	3.8%
c	2646	3.5%
Other values (35)	19705	26.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	66974	88.6%
Dash Punctuation	8419	11.1%
Decimal Number	206	0.3%
Uppercase Letter	14	< 0.1%
Other Punctuation	9	< 0.1%
Space Separator	2	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	10086	15.1%
s	6751	10.1%
i	6490	9.7%
n	5932	8.9%
a	4920	7.3%
t	4641	6.9%
b	3126	4.7%
r	2908	4.3%
c	2646	4.0%
w	2582	3.9%
Other values (15)	16892	25.2%

Uppercase Letter

Value	Count	Frequency (%)
S	3	21.4%
A	3	21.4%
U	2	14.3%
C	1	7.1%
T	1	7.1%
D	1	7.1%
H	1	7.1%
P	1	7.1%
G	1	7.1%

Decimal Number

Value	Count	Frequency (%)
4	97	47.1%
2	96	46.6%
3	8	3.9%
7	2	1.0%
1	2	1.0%
0	1	0.5%

Other Punctuation

Value	Count	Frequency (%)
/	5	55.6%
.	3	33.3%
:	1	11.1%

Dash Punctuation

Value	Count	Frequency (%)
-	8419	100.0%

Space Separator

Value	Count	Frequency (%)
	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	66988	88.6%
Common	8636	11.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	10086	15.1%
s	6751	10.1%
i	6490	9.7%
n	5932	8.9%
a	4920	7.3%
t	4641	6.9%
b	3126	4.7%
r	2908	4.3%
c	2646	3.9%
w	2582	3.9%
Other values (24)	16906	25.2%

Common

Value	Count	Frequency (%)
-	8419	97.5%
4	97	1.1%
2	96	1.1%
3	8	0.1%
/	5	0.1%
.	3	< 0.1%
7	2	< 0.1%
1	2	< 0.1%
	2	< 0.1%
:	1	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	75624	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	10086	13.3%
-	8419	11.1%
s	6751	8.9%
i	6490	8.6%
n	5932	7.8%
a	4920	6.5%
t	4641	6.1%
b	3126	4.1%
r	2908	3.8%
c	2646	3.5%
Other values (35)	19705	26.1%

source_name
Text

Distinct	2160
Distinct (%)	4.7%
Missing	13
Missing (%)	< 0.1%
Memory size	711.8 KiB

Length

Max length	63
Median length	32
Mean length	12.473025
Min length	2

Characters and Unicode

Total characters	568059
Distinct characters	84
Distinct categories	11 ?
Distinct scripts	2 ?
Distinct blocks	3 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	597 ?
Unique (%)	1.3%

Sample

1st row	International Business Times
2nd row	Prtimes.jp
3rd row	VOA News
4th row	The Indian Express
5th row	The Times of Israel

Value	Count	Frequency (%)
news	4618	6.5%
the	3538	5.0%
biztoc.com	2065	2.9%
daily	2057	2.9%
marketscreener.com	2046	2.9%
globenewswire	1786	2.5%
times	1772	2.5%
etf	1596	2.3%
forbes	1361	1.9%
of	1253	1.8%
Other values (2341)	48531	68.7%

Most occurring characters

Value	Count	Frequency (%)
e	60453	10.6%
o	42297	7.4%
r	31353	5.5%
s	31217	5.5%
i	31141	5.5%
a	26814	4.7%
n	25620	4.5%
c	25251	4.4%
	25137	4.4%
t	22856	4.0%
Other values (74)	245920	43.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	426948	75.2%
Uppercase Letter	91118	16.0%
Space Separator	25137	4.4%
Other Punctuation	20311	3.6%
Open Punctuation	1266	0.2%
Close Punctuation	1266	0.2%
Decimal Number	1202	0.2%
Dash Punctuation	768	0.1%
Math Symbol	39	< 0.1%
Final Punctuation	3	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	60453	14.2%
o	42297	9.9%
r	31353	7.3%
s	31217	7.3%
i	31141	7.3%
a	26814	6.3%
n	25620	6.0%
c	25251	5.9%
t	22856	5.4%
m	19419	4.5%
Other values (20)	110527	25.9%

Uppercase Letter

Value	Count	Frequency (%)
T	11211	12.3%
N	10505	11.5%
B	8101	8.9%
C	5965	6.5%
A	5561	6.1%
E	4902	5.4%
F	4807	5.3%
S	4718	5.2%
D	4561	5.0%
I	4455	4.9%
Other values (16)	26332	28.9%

Decimal Number

Value	Count	Frequency (%)
2	306	25.5%
4	269	22.4%
5	130	10.8%
9	120	10.0%
0	106	8.8%
1	86	7.2%
3	74	6.2%
7	62	5.2%
6	47	3.9%
8	2	0.2%

Other Punctuation

Value	Count	Frequency (%)
.	20046	98.7%
'	158	0.8%
/	62	0.3%
*	16	0.1%
!	15	0.1%
:	10	< 0.1%
&	3	< 0.1%
@	1	< 0.1%

Open Punctuation

Value	Count	Frequency (%)
[	834	65.9%
(	432	34.1%

Close Punctuation

Value	Count	Frequency (%)
]	834	65.9%
)	432	34.1%

Math Symbol

Value	Count	Frequency (%)
+	36	92.3%
\|	3	7.7%

Space Separator

Value	Count	Frequency (%)
	25137	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	768	100.0%

Final Punctuation

Value	Count	Frequency (%)
›	3	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	518066	91.2%
Common	49993	8.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	60453	11.7%
o	42297	8.2%
r	31353	6.1%
s	31217	6.0%
i	31141	6.0%
a	26814	5.2%
n	25620	4.9%
c	25251	4.9%
t	22856	4.4%
m	19419	3.7%
Other values (46)	201645	38.9%

Common

Value	Count	Frequency (%)
	25137	50.3%
.	20046	40.1%
[	834	1.7%
]	834	1.7%
-	768	1.5%
(	432	0.9%
)	432	0.9%
2	306	0.6%
4	269	0.5%
'	158	0.3%
Other values (18)	777	1.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	567888	> 99.9%
None	168	< 0.1%
Punctuation	3	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	60453	10.6%
o	42297	7.4%
r	31353	5.5%
s	31217	5.5%
i	31141	5.5%
a	26814	4.7%
n	25620	4.5%
c	25251	4.4%
	25137	4.4%
t	22856	4.0%
Other values (69)	245749	43.3%

None

Value	Count	Frequency (%)
é	112	66.7%
ü	48	28.6%
è	7	4.2%
ó	1	0.6%

Punctuation

Value	Count	Frequency (%)
›	3	100.0%

author
Text

MISSING

Distinct	11270
Distinct (%)	29.2%
Missing	7004
Missing (%)	15.4%
Memory size	711.8 KiB

Length

Max length	746
Median length	304
Mean length	19.275031
Min length	1

Characters and Unicode

Total characters	743091
Distinct characters	749
Distinct categories	19 ?
Distinct scripts	13 ?
Distinct blocks	17 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	5892 ?
Unique (%)	15.3%

Sample

1st row	Paavan MATHEMA
2nd row	webdesk@voanews.com (Agence France-Presse)
3rd row	Editorial
4th row	Jacob Magid
5th row	https://www.facebook.com/bbcworldservice/

Value	Count	Frequency (%)
news	2241	2.4%
contributor	1940	2.1%
marketbeat	1595	1.7%
john	923	1.0%
forbes	835	0.9%
staff	769	0.8%
pike	658	0.7%
the	600	0.6%
and	600	0.6%
press	521	0.6%
Other values (15217)	83713	88.7%

Most occurring characters

Value	Count	Frequency (%)
e	63818	8.6%
	57215	7.7%
a	53367	7.2%
o	43121	5.8%
r	42976	5.8%
n	40581	5.5%
i	38702	5.2%
t	36317	4.9%
s	35672	4.8%
l	23163	3.1%
Other values (739)	308159	41.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	535111	72.0%
Uppercase Letter	111248	15.0%
Space Separator	57265	7.7%
Other Punctuation	30257	4.1%
Control	2782	0.4%
Other Letter	1466	0.2%
Decimal Number	1317	0.2%
Open Punctuation	1078	0.1%
Close Punctuation	1076	0.1%
Dash Punctuation	1063	0.1%
Other values (9)	428	0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
タ	36	2.5%
リ	30	2.0%
ا	27	1.8%
ナ	27	1.8%
ی	23	1.6%
楽	22	1.5%
音	22	1.5%
र	22	1.5%
ر	21	1.4%
部	20	1.4%
Other values (435)	1216	82.9%

Lowercase Letter

Value	Count	Frequency (%)
e	63818	11.9%
a	53367	10.0%
o	43121	8.1%
r	42976	8.0%
n	40581	7.6%
i	38702	7.2%
t	36317	6.8%
s	35672	6.7%
l	23163	4.3%
c	18978	3.5%
Other values (132)	138416	25.9%

Uppercase Letter

Value	Count	Frequency (%)
S	9714	8.7%
M	8955	8.0%
A	8595	7.7%
C	7714	6.9%
B	6641	6.0%
R	6155	5.5%
N	6036	5.4%
P	5840	5.2%
T	5752	5.2%
J	5171	4.6%
Other values (80)	40675	36.6%

Other Punctuation

Value	Count	Frequency (%)
.	10255	33.9%
/	8993	29.7%
,	7762	25.7%
:	1902	6.3%
@	915	3.0%
'	219	0.7%
&	116	0.4%
!	36	0.1%
"	32	0.1%
／	8	< 0.1%
Other values (7)	19	0.1%

Decimal Number

Value	Count	Frequency (%)
2	308	23.4%
1	304	23.1%
0	173	13.1%
4	166	12.6%
3	83	6.3%
9	83	6.3%
7	74	5.6%
5	53	4.0%
8	42	3.2%
6	31	2.4%

Spacing Mark

Value	Count	Frequency (%)
ा	21	47.7%
ि	11	25.0%
ी	5	11.4%
ാ	3	6.8%
ോ	1	2.3%
ം	1	2.3%
ो	1	2.3%
ौ	1	2.3%

Nonspacing Mark

Value	Count	Frequency (%)
्	17	34.0%
̈	9	18.0%
ं	8	16.0%
ु	7	14.0%
े	4	8.0%
്	3	6.0%
ू	1	2.0%
ृ	1	2.0%

Math Symbol

Value	Count	Frequency (%)
\|	29	38.7%
>	16	21.3%
<	16	21.3%
=	8	10.7%
｜	4	5.3%
+	2	2.7%

Other Symbol

Value	Count	Frequency (%)
®	35	46.1%
�	14	18.4%
©	14	18.4%
™	13	17.1%

Space Separator

Value	Count	Frequency (%)
	57215	99.9%
	25	< 0.1%
	25	< 0.1%

Open Punctuation

Value	Count	Frequency (%)
(	1048	97.2%
[	16	1.5%
（	14	1.3%

Close Punctuation

Value	Count	Frequency (%)
)	1046	97.2%
]	16	1.5%
）	14	1.3%

Dash Punctuation

Value	Count	Frequency (%)
-	1054	99.2%
–	9	0.8%

Final Punctuation

Value	Count	Frequency (%)
’	48	96.0%
”	2	4.0%

Format

Value	Count	Frequency (%)
	8	72.7%
‌	3	27.3%

Control

Value	Count	Frequency (%)
	2782	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	67	100.0%

Modifier Letter

Value	Count	Frequency (%)
ー	53	100.0%

Initial Punctuation

Value	Count	Frequency (%)
“	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	643716	86.6%
Common	95169	12.8%
Cyrillic	1705	0.2%
Greek	938	0.1%
Han	584	0.1%
Katakana	340	< 0.1%
Devanagari	210	< 0.1%
Arabic	192	< 0.1%
Hiragana	109	< 0.1%
Hangul	76	< 0.1%
Other values (3)	52	< 0.1%

Most frequent character per script

Han

Value	Count	Frequency (%)
楽	22	3.8%
音	22	3.8%
部	20	3.4%
編	19	3.3%
集	17	2.9%
通	12	2.1%
報	10	1.7%
要	10	1.7%
買	8	1.4%
導	8	1.4%
Other values (246)	436	74.7%

Latin

Value	Count	Frequency (%)
e	63818	9.9%
a	53367	8.3%
o	43121	6.7%
r	42976	6.7%
n	40581	6.3%
i	38702	6.0%
t	36317	5.6%
s	35672	5.5%
l	23163	3.6%
c	18978	2.9%
Other values (117)	247021	38.4%

Cyrillic

Value	Count	Frequency (%)
а	242	14.2%
р	116	6.8%
н	113	6.6%
и	113	6.6%
о	102	6.0%
е	92	5.4%
к	85	5.0%
в	72	4.2%
л	68	4.0%
т	58	3.4%
Other values (49)	644	37.8%

Katakana

Value	Count	Frequency (%)
タ	36	10.6%
リ	30	8.8%
ナ	27	7.9%
サ	14	4.1%
フ	13	3.8%
ポ	13	3.8%
ミ	13	3.8%
ン	11	3.2%
ァ	11	3.2%
レ	9	2.6%
Other values (48)	163	47.9%

Common

Value	Count	Frequency (%)
	57215	60.1%
.	10255	10.8%
/	8993	9.4%
,	7762	8.2%
	2782	2.9%
:	1902	2.0%
-	1054	1.1%
(	1048	1.1%
)	1046	1.1%
@	915	1.0%
Other values (45)	2197	2.3%

Greek

Value	Count	Frequency (%)
α	118	12.6%
ο	72	7.7%
ς	70	7.5%
τ	63	6.7%
ν	63	6.7%
ρ	49	5.2%
ι	48	5.1%
λ	31	3.3%
η	31	3.3%
Σ	27	2.9%
Other values (36)	366	39.0%

Devanagari

Value	Count	Frequency (%)
र	22	10.5%
ा	21	10.0%
्	17	8.1%
श	14	6.7%
क	11	5.2%
ि	11	5.2%
म	10	4.8%
स	9	4.3%
ं	8	3.8%
त	7	3.3%
Other values (27)	80	38.1%

Hiragana

Value	Count	Frequency (%)
い	15	13.8%
お	9	8.3%
の	9	8.3%
も	8	7.3%
さ	8	7.3%
ん	7	6.4%
わ	5	4.6%
か	5	4.6%
が	4	3.7%
う	3	2.8%
Other values (22)	36	33.0%

Arabic

Value	Count	Frequency (%)
ا	27	14.1%
ی	23	12.0%
ر	21	10.9%
ن	12	6.2%
ب	11	5.7%
ل	11	5.7%
ح	9	4.7%
م	7	3.6%
ص	7	3.6%
و	6	3.1%
Other values (17)	58	30.2%

Hangul

Value	Count	Frequency (%)
김	14	18.4%
선	10	13.2%
문	10	13.2%
기	8	10.5%
부	4	5.3%
고	4	5.3%
자	4	5.3%
외	4	5.3%
영	2	2.6%
정	2	2.6%
Other values (13)	14	18.4%

Malayalam

Value	Count	Frequency (%)
്	3	13.0%
ാ	3	13.0%
ര	2	8.7%
യ	2	8.7%
ച	2	8.7%
ൻ	1	4.3%
എ	1	4.3%
ോ	1	4.3%
ഗ	1	4.3%
ം	1	4.3%
Other values (6)	6	26.1%

Hebrew

Value	Count	Frequency (%)
י	4	23.5%
א	2	11.8%
ר	2	11.8%
מ	2	11.8%
ב	1	5.9%
ת	1	5.9%
כ	1	5.9%
נ	1	5.9%
ו	1	5.9%
ל	1	5.9%

Inherited

Value	Count	Frequency (%)
̈	9	75.0%
‌	3	25.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	736653	99.1%
None	2981	0.4%
Cyrillic	1705	0.2%
CJK	584	0.1%
Katakana	395	0.1%
Devanagari	210	< 0.1%
Arabic	192	< 0.1%
Hiragana	109	< 0.1%
Punctuation	97	< 0.1%
Hangul	76	< 0.1%
Other values (7)	89	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	63818	8.7%
	57215	7.8%
a	53367	7.2%
o	43121	5.9%
r	42976	5.8%
n	40581	5.5%
i	38702	5.3%
t	36317	4.9%
s	35672	4.8%
l	23163	3.1%
Other values (78)	301721	41.0%

None

Value	Count	Frequency (%)
é	309	10.4%
á	221	7.4%
í	189	6.3%
ó	164	5.5%
ñ	162	5.4%
ü	139	4.7%
α	118	4.0%
ö	97	3.3%
ο	72	2.4%
ς	70	2.3%
Other values (116)	1440	48.3%

Cyrillic

Value	Count	Frequency (%)
а	242	14.2%
р	116	6.8%
н	113	6.6%
и	113	6.6%
о	102	6.0%
е	92	5.4%
к	85	5.0%
в	72	4.2%
л	68	4.0%
т	58	3.4%
Other values (49)	644	37.8%

Katakana

Value	Count	Frequency (%)
ー	53	13.4%
タ	36	9.1%
リ	30	7.6%
ナ	27	6.8%
サ	14	3.5%
フ	13	3.3%
ポ	13	3.3%
ミ	13	3.3%
ン	11	2.8%
ァ	11	2.8%
Other values (50)	174	44.1%

Punctuation

Value	Count	Frequency (%)
’	48	49.5%
	25	25.8%
–	9	9.3%
	8	8.2%
‌	3	3.1%
”	2	2.1%
“	2	2.1%

Arabic

Value	Count	Frequency (%)
ا	27	14.1%
ی	23	12.0%
ر	21	10.9%
ن	12	6.2%
ب	11	5.7%
ل	11	5.7%
ح	9	4.7%
م	7	3.6%
ص	7	3.6%
و	6	3.1%
Other values (17)	58	30.2%

CJK

Value	Count	Frequency (%)
楽	22	3.8%
音	22	3.8%
部	20	3.4%
編	19	3.3%
集	17	2.9%
通	12	2.1%
報	10	1.7%
要	10	1.7%
買	8	1.4%
導	8	1.4%
Other values (246)	436	74.7%

Devanagari

Value	Count	Frequency (%)
र	22	10.5%
ा	21	10.0%
्	17	8.1%
श	14	6.7%
क	11	5.2%
ि	11	5.2%
म	10	4.8%
स	9	4.3%
ं	8	3.8%
त	7	3.3%
Other values (27)	80	38.1%

Hiragana

Value	Count	Frequency (%)
い	15	13.8%
お	9	8.3%
の	9	8.3%
も	8	7.3%
さ	8	7.3%
ん	7	6.4%
わ	5	4.6%
か	5	4.6%
が	4	3.7%
う	3	2.8%
Other values (22)	36	33.0%

Hangul

Value	Count	Frequency (%)
김	14	18.4%
선	10	13.2%
문	10	13.2%
기	8	10.5%
부	4	5.3%
고	4	5.3%
자	4	5.3%
외	4	5.3%
영	2	2.6%
정	2	2.6%
Other values (13)	14	18.4%

Specials

Value	Count	Frequency (%)
�	14	100.0%

Letterlike Symbols

Value	Count	Frequency (%)
™	13	100.0%

Phonetic Ext

Value	Count	Frequency (%)
ᴏ	10	100.0%

Diacriticals

Value	Count	Frequency (%)
̈	9	100.0%

Hebrew

Value	Count	Frequency (%)
י	4	23.5%
א	2	11.8%
ר	2	11.8%
מ	2	11.8%
ב	1	5.9%
ת	1	5.9%
כ	1	5.9%
נ	1	5.9%
ו	1	5.9%
ל	1	5.9%

Malayalam

Value	Count	Frequency (%)
്	3	13.0%
ാ	3	13.0%
ര	2	8.7%
യ	2	8.7%
ച	2	8.7%
ൻ	1	4.3%
എ	1	4.3%
ോ	1	4.3%
ഗ	1	4.3%
ം	1	4.3%
Other values (6)	6	26.1%

Latin Ext Additional

Value	Count	Frequency (%)
Ễ	1	33.3%
ế	1	33.3%
ệ	1	33.3%

title
Text

Distinct	30876
Distinct (%)	67.8%
Missing	33
Missing (%)	0.1%
Memory size	711.8 KiB

Length

Max length	590
Median length	249
Mean length	71.767678
Min length	5

Characters and Unicode

Total characters	3267080
Distinct characters	2937
Distinct categories	22 ?
Distinct scripts	15 ?
Distinct blocks	27 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	24006 ?
Unique (%)	52.7%

Sample

1st row	UN Chief Urges World To 'Stop The Madness' Of Climate Change
2nd row	RANDEBOOよりワンランク上の大人っぽさが漂うニットとベストが新登場。
3rd row	UN Chief Urges World to 'Stop the Madness' of Climate Change
4th row	Sikkim warning: Hydroelectricity push must be accompanied by safety measures
5th row	200 foreigners, dual nationals cut down in Hamas assault. Here’s where they were from

Value	Count	Frequency (%)
to	11222	2.2%
the	9427	1.8%
in	8892	1.7%
of	7610	1.5%
and	6159	1.2%
for	5860	1.1%
a	5494	1.1%
	5126	1.0%
on	3781	0.7%
with	3045	0.6%
Other values (60831)	446415	87.0%

Most occurring characters

Value	Count	Frequency (%)
	466715	14.3%
e	274258	8.4%
a	208839	6.4%
i	183804	5.6%
n	177349	5.4%
o	173652	5.3%
t	168184	5.1%
r	166093	5.1%
s	162424	5.0%
l	108147	3.3%
Other values (2927)	1177615	36.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2285370	70.0%
Space Separator	467578	14.3%
Uppercase Letter	318669	9.8%
Decimal Number	60351	1.8%
Other Punctuation	55027	1.7%
Other Letter	38097	1.2%
Dash Punctuation	11769	0.4%
Final Punctuation	8202	0.3%
Open Punctuation	5462	0.2%
Close Punctuation	5327	0.2%
Other values (12)	11228	0.3%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
ン	1034	2.7%
の	931	2.4%
ス	620	1.6%
イ	600	1.6%
ト	585	1.5%
を	523	1.4%
が	502	1.3%
ラ	485	1.3%
ル	480	1.3%
に	458	1.2%
Other values (2377)	31879	83.7%

Lowercase Letter

Value	Count	Frequency (%)
e	274258	12.0%
a	208839	9.1%
i	183804	8.0%
n	177349	7.8%
o	173652	7.6%
t	168184	7.4%
r	166093	7.3%
s	162424	7.1%
l	108147	4.7%
d	77013	3.4%
Other values (199)	585607	25.6%

Uppercase Letter

Value	Count	Frequency (%)
S	30054	9.4%
A	27085	8.5%
C	21787	6.8%
T	21718	6.8%
I	17772	5.6%
M	17079	5.4%
P	15792	5.0%
B	15674	4.9%
R	15438	4.8%
E	15141	4.8%
Other values (110)	121129	38.0%

Nonspacing Mark

Value	Count	Frequency (%)
े	183	21.3%
्	152	17.7%
ं	109	12.7%
ु	52	6.1%
่	46	5.4%
้	37	4.3%
ั	31	3.6%
ี	31	3.6%
ิ	25	2.9%
ू	22	2.6%
Other values (26)	171	19.9%

Other Punctuation

Value	Count	Frequency (%)
,	14157	25.7%
:	12597	22.9%
.	8695	15.8%
'	8556	15.5%
?	2256	4.1%
"	1574	2.9%
%	1538	2.8%
&	1346	2.4%
/	790	1.4%
!	706	1.3%
Other values (24)	2812	5.1%

Decimal Number

Value	Count	Frequency (%)
2	14920	24.7%
0	13039	21.6%
1	7645	12.7%
3	7222	12.0%
5	4014	6.7%
4	3892	6.4%
9	2499	4.1%
8	2399	4.0%
6	2361	3.9%
7	2326	3.9%
Other values (15)	34	0.1%

Math Symbol

Value	Count	Frequency (%)
\|	927	57.0%
+	495	30.4%
×	46	2.8%
～	40	2.5%
＞	23	1.4%
＜	23	1.4%
~	13	0.8%
>	12	0.7%
<	12	0.7%
｜	8	0.5%
Other values (10)	27	1.7%

Spacing Mark

Value	Count	Frequency (%)
ा	283	46.4%
ी	130	21.3%
ि	102	16.7%
ो	61	10.0%
ॉ	7	1.1%
ം	5	0.8%
ौ	5	0.8%
ା	5	0.8%
ി	3	0.5%
ാ	3	0.5%
Other values (5)	6	1.0%

Other Symbol

Value	Count	Frequency (%)
™	47	33.3%
®	45	31.9%
°	12	8.5%
━	8	5.7%
★	5	3.5%
⚡	4	2.8%
⭐	4	2.8%
☆	4	2.8%
♪	3	2.1%
✴	3	2.1%
Other values (5)	6	4.3%

Open Punctuation

Value	Count	Frequency (%)
(	3176	58.1%
[	1151	21.1%
「	508	9.3%
【	178	3.3%
『	164	3.0%
„	138	2.5%
（	110	2.0%
{	14	0.3%
《	13	0.2%
｢	8	0.1%

Control

Value	Count	Frequency (%)
	38	41.8%
	27	29.7%
	8	8.8%
	7	7.7%
	4	4.4%
	2	2.2%
	1	1.1%
	1	1.1%
	1	1.1%
	1	1.1%

Close Punctuation

Value	Count	Frequency (%)
)	3181	59.7%
]	1150	21.6%
」	506	9.5%
】	178	3.3%
』	164	3.1%
）	110	2.1%
}	14	0.3%
》	14	0.3%
｣	8	0.2%
〉	2	< 0.1%

Modifier Letter

Value	Count	Frequency (%)
ー	1527	99.1%
々	5	0.3%
ـ	3	0.2%
ﾟ	2	0.1%
ｰ	1	0.1%
ʽ	1	0.1%
ʼ	1	0.1%
ๆ	1	0.1%

Dash Punctuation

Value	Count	Frequency (%)
-	10148	86.2%
–	1113	9.5%
—	489	4.2%
－	8	0.1%
〜	7	0.1%
‒	3	< 0.1%
―	1	< 0.1%

Space Separator

Value	Count	Frequency (%)
	466715	99.8%
	769	0.2%
	89	< 0.1%
	3	< 0.1%
	2	< 0.1%

Currency Symbol

Value	Count	Frequency (%)
$	2596	92.3%
€	163	5.8%
£	40	1.4%
₹	12	0.4%
¢	1	< 0.1%

Format

Value	Count	Frequency (%)
‌	20	32.8%
	14	23.0%
	14	23.0%
‍	11	18.0%
⁠	2	3.3%

Other Number

Value	Count	Frequency (%)
½	8	40.0%
¹	7	35.0%
²	3	15.0%
₂	2	10.0%

Final Punctuation

Value	Count	Frequency (%)
’	7127	86.9%
”	841	10.3%
»	234	2.9%

Initial Punctuation

Value	Count	Frequency (%)
‘	2334	67.9%
“	869	25.3%
«	234	6.8%

Modifier Symbol

Value	Count	Frequency (%)
゛	4	50.0%
^	3	37.5%
¸	1	12.5%

Connector Punctuation

Value	Count	Frequency (%)
_	22	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2583239	79.1%
Common	623431	19.1%
Cyrillic	17139	0.5%
Han	14136	0.4%
Katakana	12571	0.4%
Hiragana	6632	0.2%
Greek	3683	0.1%
Devanagari	2861	0.1%
Arabic	1172	< 0.1%
Thai	1101	< 0.1%
Other values (5)	1115	< 0.1%

Most frequent character per script

Han

Value	Count	Frequency (%)
開	292	2.1%
日	265	1.9%
新	171	1.2%
月	160	1.1%
大	153	1.1%
本	147	1.0%
年	142	1.0%
催	139	1.0%
定	126	0.9%
発	126	0.9%
Other values (1794)	12415	87.8%

Hangul

Value	Count	Frequency (%)
스	38	4.6%
업	26	3.1%
리	24	2.9%
이	21	2.5%
기	18	2.2%
트	14	1.7%
에	14	1.7%
정	13	1.6%
타	12	1.4%
시	12	1.4%
Other values (221)	640	76.9%

Latin

Value	Count	Frequency (%)
e	274258	10.6%
a	208839	8.1%
i	183804	7.1%
n	177349	6.9%
o	173652	6.7%
t	168184	6.5%
r	166093	6.4%
s	162424	6.3%
l	108147	4.2%
d	77013	3.0%
Other values (193)	883476	34.2%

Common

Value	Count	Frequency (%)
	466715	74.9%
2	14920	2.4%
,	14157	2.3%
0	13039	2.1%
:	12597	2.0%
-	10148	1.6%
.	8695	1.4%
'	8556	1.4%
1	7645	1.2%
3	7222	1.2%
Other values (150)	59737	9.6%

Katakana

Value	Count	Frequency (%)
ン	1034	8.2%
ス	620	4.9%
イ	600	4.8%
ト	585	4.7%
ラ	485	3.9%
ル	480	3.8%
ッ	426	3.4%
ア	407	3.2%
ク	386	3.1%
リ	384	3.1%
Other values (72)	7164	57.0%

Hiragana

Value	Count	Frequency (%)
の	931	14.0%
を	523	7.9%
が	502	7.6%
に	458	6.9%
で	316	4.8%
る	260	3.9%
た	225	3.4%
い	210	3.2%
し	208	3.1%
な	206	3.1%
Other values (63)	2793	42.1%

Devanagari

Value	Count	Frequency (%)
ा	283	9.9%
र	189	6.6%
े	183	6.4%
क	175	6.1%
्	152	5.3%
ी	130	4.5%
ल	118	4.1%
स	109	3.8%
ं	109	3.8%
म	104	3.6%
Other values (55)	1309	45.8%

Cyrillic

Value	Count	Frequency (%)
о	1706	10.0%
и	1405	8.2%
а	1397	8.2%
е	1214	7.1%
н	1056	6.2%
с	1010	5.9%
т	988	5.8%
р	893	5.2%
л	750	4.4%
в	715	4.2%
Other values (54)	6005	35.0%

Greek

Value	Count	Frequency (%)
α	318	8.6%
τ	309	8.4%
ο	262	7.1%
ι	259	7.0%
ε	216	5.9%
ν	198	5.4%
σ	181	4.9%
η	168	4.6%
ρ	161	4.4%
π	135	3.7%
Other values (54)	1476	40.1%

Thai

Value	Count	Frequency (%)
า	58	5.3%
น	57	5.2%
ร	56	5.1%
ง	53	4.8%
่	46	4.2%
อ	44	4.0%
เ	43	3.9%
ย	43	3.9%
ม	40	3.6%
ก	37	3.4%
Other values (45)	624	56.7%

Arabic

Value	Count	Frequency (%)
ا	171	14.6%
ر	92	7.8%
ی	89	7.6%
م	66	5.6%
و	64	5.5%
ل	61	5.2%
ن	58	4.9%
ه	56	4.8%
د	54	4.6%
ت	53	4.5%
Other values (34)	408	34.8%

Malayalam

Value	Count	Frequency (%)
്	20	19.4%
ക	8	7.8%
ന	7	6.8%
ു	6	5.8%
സ	6	5.8%
ം	5	4.9%
ി	3	2.9%
ല	3	2.9%
ര	3	2.9%
ഉ	3	2.9%
Other values (26)	39	37.9%

Hebrew

Value	Count	Frequency (%)
א	10	13.3%
ל	8	10.7%
מ	6	8.0%
ו	6	8.0%
ש	4	5.3%
ה	4	5.3%
ר	4	5.3%
ב	3	4.0%
נ	3	4.0%
ם	3	4.0%
Other values (13)	24	32.0%

Oriya

Value	Count	Frequency (%)
ି	6	15.0%
ା	5	12.5%
ର	3	7.5%
ସ	3	7.5%
ଏ	2	5.0%
୍	2	5.0%
ପ	2	5.0%
କ	2	5.0%
ବ	2	5.0%
ମ	2	5.0%
Other values (11)	11	27.5%

Inherited

Value	Count	Frequency (%)
‌	20	30.8%
‍	11	16.9%
̣	8	12.3%
️	7	10.8%
́	5	7.7%
̉	4	6.2%
̃	4	6.2%
̀	2	3.1%
ّ	1	1.5%
ِ	1	1.5%
Other values (2)	2	3.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	3176071	97.2%
None	18728	0.6%
Cyrillic	17139	0.5%
Katakana	14366	0.4%
CJK	14131	0.4%
Punctuation	13139	0.4%
Hiragana	6636	0.2%
Devanagari	2861	0.1%
Arabic	1182	< 0.1%
Thai	1101	< 0.1%
Other values (17)	1726	0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	466715	14.7%
e	274258	8.6%
a	208839	6.6%
i	183804	5.8%
n	177349	5.6%
o	173652	5.5%
t	168184	5.3%
r	166093	5.2%
s	162424	5.1%
l	108147	3.4%
Other values (85)	1086606	34.2%

Punctuation

Value	Count	Frequency (%)
’	7127	54.2%
‘	2334	17.8%
–	1113	8.5%
“	869	6.6%
”	841	6.4%
—	489	3.7%
…	153	1.2%
„	138	1.1%
‌	20	0.2%
″	15	0.1%
Other values (9)	40	0.3%

None

Value	Count	Frequency (%)
é	1994	10.6%
á	921	4.9%
ó	774	4.1%
	769	4.1%
í	539	2.9%
ü	537	2.9%
ä	526	2.8%
「	508	2.7%
」	506	2.7%
、	450	2.4%
Other values (241)	11204	59.8%

Cyrillic

Value	Count	Frequency (%)
о	1706	10.0%
и	1405	8.2%
а	1397	8.2%
е	1214	7.1%
н	1056	6.2%
с	1010	5.9%
т	988	5.8%
р	893	5.2%
л	750	4.4%
в	715	4.2%
Other values (54)	6005	35.0%

Katakana

Value	Count	Frequency (%)
ー	1527	10.6%
ン	1034	7.2%
ス	620	4.3%
イ	600	4.2%
ト	585	4.1%
ラ	485	3.4%
ル	480	3.3%
ッ	426	3.0%
ア	407	2.8%
ク	386	2.7%
Other values (72)	7816	54.4%

Hiragana

Value	Count	Frequency (%)
の	931	14.0%
を	523	7.9%
が	502	7.6%
に	458	6.9%
で	316	4.8%
る	260	3.9%
た	225	3.4%
い	210	3.2%
し	208	3.1%
な	206	3.1%
Other values (64)	2797	42.1%

CJK

Value	Count	Frequency (%)
開	292	2.1%
日	265	1.9%
新	171	1.2%
月	160	1.1%
大	153	1.1%
本	147	1.0%
年	142	1.0%
催	139	1.0%
定	126	0.9%
発	126	0.9%
Other values (1793)	12410	87.8%

Devanagari

Value	Count	Frequency (%)
ा	283	9.9%
र	189	6.6%
े	183	6.4%
क	175	6.1%
्	152	5.3%
ी	130	4.5%
ल	118	4.1%
स	109	3.8%
ं	109	3.8%
म	104	3.6%
Other values (55)	1309	45.8%

Arabic

Value	Count	Frequency (%)
ا	171	14.5%
ر	92	7.8%
ی	89	7.5%
م	66	5.6%
و	64	5.4%
ل	61	5.2%
ن	58	4.9%
ه	56	4.7%
د	54	4.6%
ت	53	4.5%
Other values (40)	418	35.4%

Currency Symbols

Value	Count	Frequency (%)
€	163	93.1%
₹	12	6.9%

Thai

Value	Count	Frequency (%)
า	58	5.3%
น	57	5.2%
ร	56	5.1%
ง	53	4.8%
่	46	4.2%
อ	44	4.0%
เ	43	3.9%
ย	43	3.9%
ม	40	3.6%
ก	37	3.4%
Other values (45)	624	56.7%

Letterlike Symbols

Value	Count	Frequency (%)
™	47	100.0%

Hangul

Value	Count	Frequency (%)
스	38	4.6%
업	26	3.1%
리	24	2.9%
이	21	2.5%
기	18	2.2%
트	14	1.7%
에	14	1.7%
정	13	1.6%
타	12	1.4%
시	12	1.4%
Other values (220)	637	76.8%

Latin Ext Additional

Value	Count	Frequency (%)
ệ	33	8.9%
ạ	28	7.5%
ế	21	5.7%
ề	20	5.4%
ố	20	5.4%
ủ	18	4.9%
ộ	16	4.3%
ở	16	4.3%
ớ	15	4.0%
ậ	15	4.0%
Other values (28)	169	45.6%

Malayalam

Value	Count	Frequency (%)
്	20	19.4%
ക	8	7.8%
ന	7	6.8%
ു	6	5.8%
സ	6	5.8%
ം	5	4.9%
ി	3	2.9%
ല	3	2.9%
ര	3	2.9%
ഉ	3	2.9%
Other values (26)	39	37.9%

Hebrew

Value	Count	Frequency (%)
א	10	13.3%
ל	8	10.7%
מ	6	8.0%
ו	6	8.0%
ש	4	5.3%
ה	4	5.3%
ר	4	5.3%
ב	3	4.0%
נ	3	4.0%
ם	3	4.0%
Other values (13)	24	32.0%

Math Operators

Value	Count	Frequency (%)
−	8	57.1%
≫	2	14.3%
≪	2	14.3%
∽	1	7.1%
∀	1	7.1%

Diacriticals

Value	Count	Frequency (%)
̣	8	34.8%
́	5	21.7%
̉	4	17.4%
̃	4	17.4%
̀	2	8.7%

Box Drawing

Value	Count	Frequency (%)
━	8	80.0%
─	2	20.0%

VS

Value	Count	Frequency (%)
️	7	87.5%
︎	1	12.5%

Oriya

Value	Count	Frequency (%)
ି	6	15.0%
ା	5	12.5%
ର	3	7.5%
ସ	3	7.5%
ଏ	2	5.0%
୍	2	5.0%
ପ	2	5.0%
କ	2	5.0%
ବ	2	5.0%
ମ	2	5.0%
Other values (11)	11	27.5%

Misc Symbols

Value	Count	Frequency (%)
★	5	29.4%
⚡	4	23.5%
☆	4	23.5%
♪	3	17.6%
⚠	1	5.9%

Compat Jamo

Value	Count	Frequency (%)
ㆍ	3	100.0%

Dingbats

Value	Count	Frequency (%)
✴	3	60.0%
✕	1	20.0%
✚	1	20.0%

Arrows

Value	Count	Frequency (%)
→	2	66.7%
⇒	1	33.3%

Geometric Shapes

Value	Count	Frequency (%)
▶	1	100.0%

Modifier Letters

Value	Count	Frequency (%)
ʽ	1	50.0%
ʼ	1	50.0%

description
Text

Distinct	29677
Distinct (%)	65.4%
Missing	195
Missing (%)	0.4%
Memory size	711.8 KiB

Length

Max length	260
Median length	250
Mean length	195.86715
Min length	3

Characters and Unicode

Total characters	8884730
Distinct characters	3774
Distinct categories	23 ?
Distinct scripts	15 ?
Distinct blocks	30 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	21575 ?
Unique (%)	47.6%

Sample

1st row	UN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastating impact of the phenomenon.
2nd row	[株式会社Ainer] RANDEBOO（ランデブー）では2023年7月18日(火)より公式WEBストアにて2023 Autumn Winter コレクションの発売を開始。シーズンコレクションとして、"Nepal Handmade ram vest"と”Nepal Handmade ram kn...
3rd row	UN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastating impact of the phenomenon. "The rooftops of the world are…
4th row	Ecologists caution against the adverse effects of dam construction — it increases the volatility of rocks in the Himalayan region. Wednesday's disaster is a warning to take such caveats seriously
5th row	France lost 35 citizens, Thailand 33, US 31, Ukraine 21, Russia 19, UK 12, Nepal 10, Germany 10, Argentina 9, Canada 6, Romania 5, Portuguese 4, China 4, Philippines 4, Austria 4 The post 200 foreigners, dual nationals cut down in Hamas assault. Here’s where …

Value	Count	Frequency (%)
the	66415	4.8%
of	30101	2.2%
a	29290	2.1%
to	28018	2.0%
and	27170	1.9%
in	26056	1.9%
on	13798	1.0%
for	12671	0.9%
is	11329	0.8%
with	9211	0.7%
Other values (108309)	1143258	81.8%

Most occurring characters

Value	Count	Frequency (%)
	1352358	15.2%
e	800667	9.0%
a	573582	6.5%
t	524220	5.9%
i	498717	5.6%
n	494673	5.6%
o	478325	5.4%
r	445751	5.0%
s	431581	4.9%
l	275603	3.1%
Other values (3764)	3009253	33.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6487557	73.0%
Space Separator	1357042	15.3%
Uppercase Letter	480236	5.4%
Other Punctuation	215454	2.4%
Decimal Number	134538	1.5%
Other Letter	90478	1.0%
Dash Punctuation	31272	0.4%
Control	25076	0.3%
Open Punctuation	15536	0.2%
Close Punctuation	14870	0.2%
Other values (13)	32671	0.4%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
の	1994	2.2%
ン	1689	1.9%
を	1074	1.2%
に	1046	1.2%
し	1014	1.1%
ス	990	1.1%
イ	963	1.1%
ト	929	1.0%
で	888	1.0%
日	856	0.9%
Other values (3149)	79035	87.4%

Lowercase Letter

Value	Count	Frequency (%)
e	800667	12.3%
a	573582	8.8%
t	524220	8.1%
i	498717	7.7%
n	494673	7.6%
o	478325	7.4%
r	445751	6.9%
s	431581	6.7%
l	275603	4.2%
d	246473	3.8%
Other values (212)	1717965	26.5%

Uppercase Letter

Value	Count	Frequency (%)
S	41001	8.5%
T	40731	8.5%
A	40568	8.4%
C	31751	6.6%
I	29157	6.1%
E	25403	5.3%
M	23792	5.0%
N	23592	4.9%
R	22873	4.8%
P	22002	4.6%
Other values (120)	179366	37.3%

Other Punctuation

Value	Count	Frequency (%)
.	74857	34.7%
,	69202	32.1%
…	21113	9.8%
'	11972	5.6%
:	9064	4.2%
/	7987	3.7%
"	5789	2.7%
%	3014	1.4%
?	2076	1.0%
、	1881	0.9%
Other values (32)	8499	3.9%

Nonspacing Mark

Value	Count	Frequency (%)
े	468	22.0%
्	308	14.5%
ं	246	11.6%
่	153	7.2%
ั	105	4.9%
้	101	4.7%
ु	98	4.6%
ै	91	4.3%
ี	86	4.0%
ิ	66	3.1%
Other values (26)	406	19.1%

Other Symbol

Value	Count	Frequency (%)
®	97	23.8%
™	73	17.9%
©	52	12.8%
■	47	11.5%
°	33	8.1%
▸	20	4.9%
●	18	4.4%
◆	14	3.4%
▼	6	1.5%
★	5	1.2%
Other values (25)	42	10.3%

Decimal Number

Value	Count	Frequency (%)
2	32108	23.9%
0	27221	20.2%
1	19618	14.6%
3	15002	11.2%
5	8574	6.4%
4	8347	6.2%
9	6352	4.7%
6	5944	4.4%
8	5731	4.3%
7	5557	4.1%
Other values (16)	84	0.1%

Spacing Mark

Value	Count	Frequency (%)
ा	689	41.4%
ी	346	20.8%
ि	282	16.9%
ो	205	12.3%
ി	28	1.7%
ौ	19	1.1%
ാ	18	1.1%
ା	14	0.8%
ം	13	0.8%
େ	11	0.7%
Other values (9)	39	2.3%

Math Symbol

Value	Count	Frequency (%)
<	1042	30.7%
>	1036	30.6%
+	761	22.4%
\|	199	5.9%
=	110	3.2%
～	76	2.2%
~	33	1.0%
→	28	0.8%
×	24	0.7%
＜	23	0.7%
Other values (8)	59	1.7%

Open Punctuation

Value	Count	Frequency (%)
(	10922	70.3%
[	2585	16.6%
「	786	5.1%
（	637	4.1%
„	247	1.6%
『	182	1.2%
【	91	0.6%
{	40	0.3%
《	36	0.2%
｢	5	< 0.1%
Other values (3)	5	< 0.1%

Control

Value	Count	Frequency (%)
	24347	97.1%
	650	2.6%
	41	0.2%
	24	0.1%
	3	< 0.1%
	3	< 0.1%
	2	< 0.1%
	2	< 0.1%
	1	< 0.1%
	1	< 0.1%
Other values (2)	2	< 0.1%

Close Punctuation

Value	Count	Frequency (%)
)	10720	72.1%
]	2513	16.9%
」	721	4.8%
）	591	4.0%
』	169	1.1%
】	88	0.6%
》	38	0.3%
}	20	0.1%
｣	5	< 0.1%
］	3	< 0.1%
Other values (2)	2	< 0.1%

Space Separator

Value	Count	Frequency (%)
	1352358	99.7%
	4475	0.3%
	189	< 0.1%
	10	< 0.1%
	6	< 0.1%
	2	< 0.1%
	1	< 0.1%
	1	< 0.1%

Modifier Letter

Value	Count	Frequency (%)
ー	2545	98.6%
々	24	0.9%
ๆ	4	0.2%
ﾞ	3	0.1%
ـ	3	0.1%
ᵉ	2	0.1%
ˈ	1	< 0.1%

Format

Value	Count	Frequency (%)
‌	69	43.4%
	44	27.7%
‍	24	15.1%
	9	5.7%
	5	3.1%
⁠	4	2.5%
‎	4	2.5%

Dash Punctuation

Value	Count	Frequency (%)
-	27059	86.5%
–	2807	9.0%
—	1391	4.4%
〜	7	< 0.1%
―	6	< 0.1%
－	2	< 0.1%

Currency Symbol

Value	Count	Frequency (%)
$	3808	91.6%
€	216	5.2%
£	87	2.1%
₹	43	1.0%
¥	1	< 0.1%
¢	1	< 0.1%

Final Punctuation

Value	Count	Frequency (%)
’	11092	80.3%
”	2437	17.6%
»	265	1.9%
›	22	0.2%

Modifier Symbol

Value	Count	Frequency (%)
^	20	51.3%
`	12	30.8%
゛	4	10.3%
´	3	7.7%

Initial Punctuation

Value	Count	Frequency (%)
“	3066	74.6%
‘	756	18.4%
«	288	7.0%

Other Number

Value	Count	Frequency (%)
²	12	70.6%
½	4	23.5%
₂	1	5.9%

Connector Punctuation

Value	Count	Frequency (%)
_	200	100.0%

Letter Number

Value	Count	Frequency (%)
〇	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	6911788	77.8%
Common	1822499	20.5%
Cyrillic	45840	0.5%
Han	36460	0.4%
Katakana	20825	0.2%
Hiragana	18127	0.2%
Greek	10205	0.1%
Devanagari	7320	0.1%
Hangul	3922	< 0.1%
Arabic	3625	< 0.1%
Other values (5)	4119	< 0.1%

Most frequent character per script

Han

Value	Count	Frequency (%)
日	856	2.3%
社	641	1.8%
会	585	1.6%
月	573	1.6%
式	504	1.4%
年	488	1.3%
株	436	1.2%
開	405	1.1%
画	400	1.1%
的	387	1.1%
Other values (2348)	31185	85.5%

Hangul

Value	Count	Frequency (%)
이	102	2.6%
스	93	2.4%
업	83	2.1%
는	70	1.8%
자	68	1.7%
다	67	1.7%
대	57	1.5%
를	57	1.5%
로	57	1.5%
을	54	1.4%
Other values (398)	3214	81.9%

Latin

Value	Count	Frequency (%)
e	800667	11.6%
a	573582	8.3%
t	524220	7.6%
i	498717	7.2%
n	494673	7.2%
o	478325	6.9%
r	445751	6.4%
s	431581	6.2%
l	275603	4.0%
d	246473	3.6%
Other values (214)	2142196	31.0%

Common

Value	Count	Frequency (%)
	1352358	74.2%
.	74857	4.1%
,	69202	3.8%
2	32108	1.8%
0	27221	1.5%
-	27059	1.5%
	24347	1.3%
…	21113	1.2%
1	19618	1.1%
3	15002	0.8%
Other values (177)	159614	8.8%

Katakana

Value	Count	Frequency (%)
ン	1689	8.1%
ス	990	4.8%
イ	963	4.6%
ト	929	4.5%
ル	832	4.0%
ラ	748	3.6%
ッ	680	3.3%
ア	657	3.2%
リ	625	3.0%
ク	622	3.0%
Other values (76)	12090	58.1%

Hiragana

Value	Count	Frequency (%)
の	1994	11.0%
を	1074	5.9%
に	1046	5.8%
し	1014	5.6%
で	888	4.9%
た	841	4.6%
が	834	4.6%
は	814	4.5%
る	765	4.2%
と	700	3.9%
Other values (65)	8157	45.0%

Cyrillic

Value	Count	Frequency (%)
о	4691	10.2%
и	3743	8.2%
а	3688	8.0%
е	3587	7.8%
н	3013	6.6%
т	2748	6.0%
с	2584	5.6%
р	2481	5.4%
в	1909	4.2%
л	1881	4.1%
Other values (57)	15515	33.8%

Greek

Value	Count	Frequency (%)
α	940	9.2%
τ	823	8.1%
ο	752	7.4%
ι	717	7.0%
ε	626	6.1%
ν	567	5.6%
σ	483	4.7%
ρ	450	4.4%
η	401	3.9%
κ	387	3.8%
Other values (54)	4059	39.8%

Devanagari

Value	Count	Frequency (%)
ा	689	9.4%
र	482	6.6%
क	471	6.4%
े	468	6.4%
ी	346	4.7%
्	308	4.2%
न	305	4.2%
स	289	3.9%
ि	282	3.9%
ल	268	3.7%
Other values (52)	3412	46.6%

Thai

Value	Count	Frequency (%)
า	174	5.7%
น	170	5.6%
ง	164	5.4%
่	153	5.0%
ร	152	5.0%
อ	131	4.3%
ก	129	4.2%
เ	112	3.7%
ม	107	3.5%
ั	105	3.4%
Other values (52)	1648	54.1%

Arabic

Value	Count	Frequency (%)
ا	511	14.1%
ی	268	7.4%
م	236	6.5%
ر	226	6.2%
ن	220	6.1%
ل	212	5.8%
و	207	5.7%
د	203	5.6%
ه	173	4.8%
ت	171	4.7%
Other values (46)	1198	33.0%

Malayalam

Value	Count	Frequency (%)
്	60	14.6%
ി	28	6.8%
ക	28	6.8%
യ	26	6.3%
ു	23	5.6%
ന	20	4.9%
ാ	18	4.4%
സ	18	4.4%
ട	15	3.6%
ം	13	3.2%
Other values (41)	162	39.4%

Oriya

Value	Count	Frequency (%)
ି	15	8.7%
ା	14	8.1%
େ	11	6.4%
ସ	11	6.4%
କ	10	5.8%
୍	9	5.2%
ର	9	5.2%
ଏ	9	5.2%
ହ	6	3.5%
ୟ	6	3.5%
Other values (28)	73	42.2%

Hebrew

Value	Count	Frequency (%)
ו	35	10.6%
י	34	10.3%
ה	28	8.5%
ר	26	7.9%
מ	25	7.6%
א	25	7.6%
ש	22	6.6%
ל	20	6.0%
ב	18	5.4%
ח	16	4.8%
Other values (13)	82	24.8%

Inherited

Value	Count	Frequency (%)
‌	69	43.4%
‍	24	15.1%
️	19	11.9%
́	13	8.2%
̣	10	6.3%
̀	8	5.0%
̂	5	3.1%
ّ	3	1.9%
ُ	2	1.3%
ً	2	1.3%
Other values (3)	4	2.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8646544	97.3%
None	50013	0.6%
Cyrillic	45840	0.5%
Punctuation	43271	0.5%
CJK	36424	0.4%
Katakana	23846	0.3%
Hiragana	18131	0.2%
Devanagari	7347	0.1%
Hangul	3922	< 0.1%
Arabic	3635	< 0.1%
Other values (20)	5757	0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1352358	15.6%
e	800667	9.3%
a	573582	6.6%
t	524220	6.1%
i	498717	5.8%
n	494673	5.7%
o	478325	5.5%
r	445751	5.2%
s	431581	5.0%
l	275603	3.2%
Other values (87)	2771067	32.0%

Punctuation

Value	Count	Frequency (%)
…	21113	48.8%
’	11092	25.6%
“	3066	7.1%
–	2807	6.5%
”	2437	5.6%
—	1391	3.2%
‘	756	1.7%
„	247	0.6%
•	115	0.3%
‌	69	0.2%
Other values (14)	178	0.4%

None

Value	Count	Frequency (%)
é	5463	10.9%
	4475	8.9%
ó	2107	4.2%
á	2094	4.2%
、	1881	3.8%
ä	1542	3.1%
í	1473	2.9%
ü	1432	2.9%
。	1028	2.1%
ö	1023	2.0%
Other values (277)	27495	55.0%

Cyrillic

Value	Count	Frequency (%)
о	4691	10.2%
и	3743	8.2%
а	3688	8.0%
е	3587	7.8%
н	3013	6.6%
т	2748	6.0%
с	2584	5.6%
р	2481	5.4%
в	1909	4.2%
л	1881	4.1%
Other values (57)	15515	33.8%

Katakana

Value	Count	Frequency (%)
ー	2545	10.7%
ン	1689	7.1%
ス	990	4.2%
イ	963	4.0%
ト	929	3.9%
ル	832	3.5%
ラ	748	3.1%
ッ	680	2.9%
ア	657	2.8%
リ	625	2.6%
Other values (74)	13188	55.3%

Hiragana

Value	Count	Frequency (%)
の	1994	11.0%
を	1074	5.9%
に	1046	5.8%
し	1014	5.6%
で	888	4.9%
た	841	4.6%
が	834	4.6%
は	814	4.5%
る	765	4.2%
と	700	3.9%
Other values (66)	8161	45.0%

CJK

Value	Count	Frequency (%)
日	856	2.4%
社	641	1.8%
会	585	1.6%
月	573	1.6%
式	504	1.4%
年	488	1.3%
株	436	1.2%
開	405	1.1%
画	400	1.1%
的	387	1.1%
Other values (2338)	31149	85.5%

Devanagari

Value	Count	Frequency (%)
ा	689	9.4%
र	482	6.6%
क	471	6.4%
े	468	6.4%
ी	346	4.7%
्	308	4.2%
न	305	4.2%
स	289	3.9%
ि	282	3.8%
ल	268	3.6%
Other values (53)	3439	46.8%

Arabic

Value	Count	Frequency (%)
ا	511	14.1%
ی	268	7.4%
م	236	6.5%
ر	226	6.2%
ن	220	6.1%
ل	212	5.8%
و	207	5.7%
د	203	5.6%
ه	173	4.8%
ت	171	4.7%
Other values (43)	1208	33.2%

Currency Symbols

Value	Count	Frequency (%)
€	216	83.4%
₹	43	16.6%

Thai

Value	Count	Frequency (%)
า	174	5.7%
น	170	5.6%
ง	164	5.4%
่	153	5.0%
ร	152	5.0%
อ	131	4.3%
ก	129	4.2%
เ	112	3.7%
ม	107	3.5%
ั	105	3.4%
Other values (52)	1648	54.1%

Hangul

Value	Count	Frequency (%)
이	102	2.6%
스	93	2.4%
업	83	2.1%
는	70	1.8%
자	68	1.7%
다	67	1.7%
대	57	1.5%
를	57	1.5%
로	57	1.5%
을	54	1.4%
Other values (398)	3214	81.9%

Latin Ext Additional

Value	Count	Frequency (%)
ạ	98	8.0%
ế	95	7.8%
ớ	85	7.0%
ộ	78	6.4%
ệ	66	5.4%
ố	62	5.1%
ả	56	4.6%
ủ	52	4.3%
ể	51	4.2%
ầ	49	4.0%
Other values (35)	526	43.2%

Letterlike Symbols

Value	Count	Frequency (%)
™	73	98.6%
℠	1	1.4%

Malayalam

Value	Count	Frequency (%)
്	60	14.6%
ി	28	6.8%
ക	28	6.8%
യ	26	6.3%
ു	23	5.6%
ന	20	4.9%
ാ	18	4.4%
സ	18	4.4%
ട	15	3.6%
ം	13	3.2%
Other values (41)	162	39.4%

Geometric Shapes

Value	Count	Frequency (%)
■	47	40.9%
▸	20	17.4%
●	18	15.7%
◆	14	12.2%
▼	6	5.2%
▲	2	1.7%
▶	2	1.7%
►	2	1.7%
◇	2	1.7%
◼	1	0.9%

Hebrew

Value	Count	Frequency (%)
ו	35	10.6%
י	34	10.3%
ה	28	8.5%
ר	26	7.9%
מ	25	7.6%
א	25	7.6%
ש	22	6.6%
ל	20	6.0%
ב	18	5.4%
ח	16	4.8%
Other values (13)	82	24.8%

Arrows

Value	Count	Frequency (%)
→	28	84.8%
↓	4	12.1%
←	1	3.0%

VS

Value	Count	Frequency (%)
️	19	95.0%
︎	1	5.0%

Oriya

Value	Count	Frequency (%)
ି	15	8.7%
ା	14	8.1%
େ	11	6.4%
ସ	11	6.4%
କ	10	5.8%
୍	9	5.2%
ର	9	5.2%
ଏ	9	5.2%
ହ	6	3.5%
ୟ	6	3.5%
Other values (28)	73	42.2%

Diacriticals

Value	Count	Frequency (%)
́	13	33.3%
̣	10	25.6%
̀	8	20.5%
̂	5	12.8%
̉	2	5.1%
̃	1	2.6%

Misc Symbols

Value	Count	Frequency (%)
★	5	62.5%
♥	2	25.0%
☆	1	12.5%

Dingbats

Value	Count	Frequency (%)
✔	4	28.6%
✴	4	28.6%
✨	2	14.3%
❤	2	14.3%
✕	1	7.1%
✈	1	7.1%

CJK Radicals Sup

Value	Count	Frequency (%)
⻑	2	100.0%

Kangxi

Value	Count	Frequency (%)
⿊	2	25.0%
⼀	1	12.5%
⽇	1	12.5%
⽉	1	12.5%
⾐	1	12.5%
⾮	1	12.5%
⽩	1	12.5%

Phonetic Ext

Value	Count	Frequency (%)
ᵉ	2	100.0%

Block Elements

Value	Count	Frequency (%)
▍	2	100.0%

IPA Ext

Value	Count	Frequency (%)
ɛ	1	100.0%

Modifier Letters

Value	Count	Frequency (%)
ˈ	1	100.0%

Enclosed Alphanum

Value	Count	Frequency (%)
Ⓡ	1	100.0%

url
Text

Distinct	30688
Distinct (%)	67.4%
Missing	14
Missing (%)	< 0.1%
Memory size	711.8 KiB

Length

Max length	439
Median length	225
Mean length	99.683633
Min length	19

Characters and Unicode

Total characters	4539792
Distinct characters	139
Distinct categories	15 ?
Distinct scripts	4 ?
Distinct blocks	5 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	22844 ?
Unique (%)	50.2%

Sample

1st row	https://www.ibtimes.com/un-chief-urges-world-stop-madness-climate-change-3716939
2nd row	https://prtimes.jp/main/html/rd/p/000000147.000032220.html
3rd row	https://www.voanews.com/a/un-chief-urges-world-to-stop-the-madness-of-climate-change-/7332605.html
4th row	https://indianexpress.com/article/opinion/editorials/sikkim-warning-hydroelectricity-push-must-be-accompanied-by-safety-measures-8970365/
5th row	https://www.timesofisrael.com/200-foreigners-dual-nationals-cut-down-in-hamas-assault-heres-where-they-were-from/

Value	Count	Frequency (%)
https://removed.com	834	1.8%
https://www.iphoneincanada.ca/2023/11/21/freedom-mobile-roam-beyond	26	0.1%
https://time.com/6337364/top-100-photos-2023	26	0.1%
https://www.bitpipe.com/detail/res/1700486941_181.html	26	0.1%
https://loyaltylobby.com/2023/11/21/qatar-airways-buy-avios-50-bonus-through-november-27-2023	22	< 0.1%
https://www.gamesindustry.biz/assassins-creed-spider-man-mario-and-ps5-boost-sales-in-europe-during-october-european-monthly-charts	21	< 0.1%
https://skift.com/2023/11/20/vietnam-looks-to-offer-visa-free-entry-to-indians-india-report	20	< 0.1%
https://deadline.com/2023/11/2023-ida-documentary-awards-nominations-announcement-1235630865	20	< 0.1%
https://blog.frequentflyerbonuses.com/2023/11/best-western-rewards-100-travel-card.html	20	< 0.1%
https://travelingformiles.com/qatar-airways-black-friday-sale-up-to-10000-bonus-avios-offer-europe	19	< 0.1%
Other values (30680)	44510	97.7%

Most occurring characters

Value	Count	Frequency (%)
-	374357	8.2%
e	337422	7.4%
t	299547	6.6%
s	264034	5.8%
/	260370	5.7%
a	242719	5.3%
o	232317	5.1%
i	216450	4.8%
n	211948	4.7%
r	196646	4.3%
Other values (129)	1903982	41.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	3266569	72.0%
Decimal Number	403533	8.9%
Other Punctuation	401897	8.9%
Dash Punctuation	374357	8.2%
Uppercase Letter	87739	1.9%
Connector Punctuation	4245	0.1%
Math Symbol	966	< 0.1%
Other Letter	424	< 0.1%
Control	28	< 0.1%
Space Separator	17	< 0.1%
Other values (5)	17	< 0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
ا	63	14.9%
ی	49	11.6%
ر	39	9.2%
ت	27	6.4%
ه	26	6.1%
ن	25	5.9%
و	24	5.7%
ب	22	5.2%
د	20	4.7%
م	19	4.5%
Other values (23)	110	25.9%

Uppercase Letter

Value	Count	Frequency (%)
A	8312	9.5%
S	7540	8.6%
E	6192	7.1%
R	5821	6.6%
I	5637	6.4%
C	5443	6.2%
T	4572	5.2%
N	4433	5.1%
M	4225	4.8%
O	4080	4.7%
Other values (20)	31484	35.9%

Lowercase Letter

Value	Count	Frequency (%)
e	337422	10.3%
t	299547	9.2%
s	264034	8.1%
a	242719	7.4%
o	232317	7.1%
i	216450	6.6%
n	211948	6.5%
r	196646	6.0%
c	151028	4.6%
w	140640	4.3%
Other values (19)	973818	29.8%

Other Punctuation

Value	Count	Frequency (%)
/	260370	64.8%
.	91789	22.8%
:	45569	11.3%
%	2829	0.7%
?	565	0.1%
&	286	0.1%
;	180	< 0.1%
,	158	< 0.1%
#	132	< 0.1%
!	15	< 0.1%
Other values (2)	4	< 0.1%

Decimal Number

Value	Count	Frequency (%)
2	85558	21.2%
1	73848	18.3%
0	62626	15.5%
3	45247	11.2%
4	27313	6.8%
5	24265	6.0%
7	22099	5.5%
8	21690	5.4%
9	20624	5.1%
6	20263	5.0%

Control

Value	Count	Frequency (%)
	6	21.4%
	5	17.9%
	5	17.9%
	5	17.9%
	2	7.1%
	2	7.1%
	2	7.1%
	1	3.6%

Math Symbol

Value	Count	Frequency (%)
=	880	91.1%
~	56	5.8%
+	23	2.4%
±	3	0.3%
−	3	0.3%
¬	1	0.1%

Modifier Symbol

Value	Count	Frequency (%)
¯	5	55.6%
¨	2	22.2%
´	2	22.2%

Other Symbol

Value	Count	Frequency (%)
°	2	66.7%
©	1	33.3%

Dash Punctuation

Value	Count	Frequency (%)
-	374357	100.0%

Connector Punctuation

Value	Count	Frequency (%)
_	4245	100.0%

Space Separator

Value	Count	Frequency (%)
	17	100.0%

Other Number

Value	Count	Frequency (%)
²	3	100.0%

Currency Symbol

Value	Count	Frequency (%)
¢	1	100.0%

Format

Value	Count	Frequency (%)
‌	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	3354308	73.9%
Common	1185061	26.1%
Arabic	422	< 0.1%
Inherited	1	< 0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	337422	10.1%
t	299547	8.9%
s	264034	7.9%
a	242719	7.2%
o	232317	6.9%
i	216450	6.5%
n	211948	6.3%
r	196646	5.9%
c	151028	4.5%
w	140640	4.2%
Other values (49)	1061557	31.6%

Common

Value	Count	Frequency (%)
-	374357	31.6%
/	260370	22.0%
.	91789	7.7%
2	85558	7.2%
1	73848	6.2%
0	62626	5.3%
:	45569	3.8%
3	45247	3.8%
4	27313	2.3%
5	24265	2.0%
Other values (37)	94119	7.9%

Arabic

Value	Count	Frequency (%)
ا	63	14.9%
ی	49	11.6%
ر	39	9.2%
ت	27	6.4%
ه	26	6.2%
ن	25	5.9%
و	24	5.7%
ب	22	5.2%
د	20	4.7%
م	19	4.5%
Other values (22)	108	25.6%

Inherited

Value	Count	Frequency (%)
‌	1	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	4539277	> 99.9%
Arabic	422	< 0.1%
None	89	< 0.1%
Math Operators	3	< 0.1%
Punctuation	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
-	374357	8.2%
e	337422	7.4%
t	299547	6.6%
s	264034	5.8%
/	260370	5.7%
a	242719	5.3%
o	232317	5.1%
i	216450	4.8%
n	211948	4.7%
r	196646	4.3%
Other values (71)	1903467	41.9%

Arabic

Value	Count	Frequency (%)
ا	63	14.9%
ی	49	11.6%
ر	39	9.2%
ت	27	6.4%
ه	26	6.2%
ن	25	5.9%
و	24	5.7%
ب	22	5.2%
د	20	4.7%
م	19	4.5%
Other values (22)	108	25.6%

None

Value	Count	Frequency (%)
Ø	22	24.7%
Ù	9	10.1%
Û	7	7.9%
	6	6.7%
¯	5	5.6%
	5	5.6%
±	3	3.4%
²	3	3.4%
Ú	3	3.4%
§	3	3.4%
Other values (14)	23	25.8%

Math Operators

Value	Count	Frequency (%)
−	3	100.0%

Punctuation

Value	Count	Frequency (%)
‌	1	100.0%

url_to_image
Text

MISSING

Distinct	25345
Distinct (%)	59.3%
Missing	2826
Missing (%)	6.2%
Memory size	711.8 KiB

Length

Max length	1105
Median length	324
Mean length	105.04535
Min length	25

Characters and Unicode

Total characters	4488588
Distinct characters	228
Distinct categories	16 ?
Distinct scripts	10 ?
Distinct blocks	12 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	18436 ?
Unique (%)	43.1%

Sample

1st row	https://d.ibtimes.com/en/full/4496078/nepals-glaciers-melted-65-percent-faster-last-decade-previous-one-said-guterres-who.jpg
2nd row	https://prtimes.jp/i/32220/147/ogp/d32220-147-21916971e381da87f7a9-0.jpg
3rd row	https://gdb.voanews.com/01000000-0a00-0242-60fd-08dbd93618b1_w1200_r1.jpg
4th row	https://images.indianexpress.com/2023/10/edit-4.jpg
5th row	https://static.timesofisrael.com/www/uploads/2023/10/231011160737-emily-hand-beeri-victim.jpg

Value	Count	Frequency (%)
https://www.marketscreener.com/images/twitter_ms_fdnoir.png	939	2.2%
https://www.marketscreener.com/images/twitter_ms_fdblanc.png	871	2.0%
https://cdn08.allafrica.com/static/images/structure/aa-logo-rgba-no-text-square.png	350	0.8%
https://s3.amazonaws.com/images.investorsobserver.com/io-logo-800x450.jpg	210	0.5%
https://ml.globenewswire.com/resource/download/b27a94e8-407e-46ae-a5c2-47bb2a1c8a2e	200	0.5%
https://ml.globenewswire.com/resource/download/908fb457-7f8e-4a08-9081-5565e3dfb3d7	163	0.4%
https://sportshub.cbsistatic.com/i/r/2019/11/06/f93a1691-1afb-4ff8-bc0e-06a49a5126f9/thumbnail/1200x675/cbb0b742977fbfc4f6ee3839bfe83e44/college-basketball-rim.jpg	125	0.3%
https://ml.globenewswire.com/resource/download/8825ef83-644c-4c64-a208-863167237797	119	0.3%
https://politicalwire.com/wp-content/uploads/2018/02/pw-podcast-logo.jpg	113	0.3%
https://ml.globenewswire.com/resource/download/ced67b94-5d6b-4532-b365-e1f1f26f8f16	109	0.3%
Other values (25410)	39657	92.5%

Most occurring characters

Value	Count	Frequency (%)
/	292670	6.5%
t	252433	5.6%
e	237993	5.3%
s	193173	4.3%
o	182977	4.1%
a	180438	4.0%
p	164093	3.7%
i	159819	3.6%
c	158991	3.5%
0	152961	3.4%
Other values (218)	2513040	56.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2722491	60.7%
Decimal Number	852181	19.0%
Other Punctuation	543551	12.1%
Uppercase Letter	147900	3.3%
Dash Punctuation	139312	3.1%
Connector Punctuation	46423	1.0%
Math Symbol	34074	0.8%
Close Punctuation	1187	< 0.1%
Open Punctuation	1187	< 0.1%
Space Separator	126	< 0.1%
Other values (6)	156	< 0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
午	5	4.0%
上	5	4.0%
屏	5	4.0%
截	5	4.0%
ョ	3	2.4%
ッ	3	2.4%
ト	3	2.4%
シ	3	2.4%
ン	3	2.4%
リ	3	2.4%
Other values (72)	86	69.4%

Lowercase Letter

Value	Count	Frequency (%)
t	252433	9.3%
e	237993	8.7%
s	193173	7.1%
o	182977	6.7%
a	180438	6.6%
p	164093	6.0%
i	159819	5.9%
c	158991	5.8%
n	131682	4.8%
m	119893	4.4%
Other values (46)	940999	34.6%

Uppercase Letter

Value	Count	Frequency (%)
A	10029	6.8%
C	9239	6.2%
F	8951	6.1%
S	8855	6.0%
M	8166	5.5%
R	7742	5.2%
D	7582	5.1%
P	7376	5.0%
T	6976	4.7%
E	6731	4.6%
Other values (28)	66253	44.8%

Other Punctuation

Value	Count	Frequency (%)
/	292670	53.8%
.	141768	26.1%
:	49132	9.0%
&	19078	3.5%
,	16442	3.0%
?	12821	2.4%
%	10603	2.0%
!	430	0.1%
;	174	< 0.1%
"	168	< 0.1%
Other values (6)	265	< 0.1%

Decimal Number

Value	Count	Frequency (%)
0	152961	17.9%
2	137747	16.2%
1	129942	15.2%
3	80972	9.5%
4	65328	7.7%
6	64475	7.6%
5	61166	7.2%
7	53829	6.3%
8	53513	6.3%
9	52248	6.1%

Math Symbol

Value	Count	Frequency (%)
=	33470	98.2%
+	560	1.6%
~	33	0.1%
×	7	< 0.1%
\|	4	< 0.1%

Close Punctuation

Value	Count	Frequency (%)
)	1128	95.0%
]	52	4.4%
}	6	0.5%
】	1	0.1%

Open Punctuation

Value	Count	Frequency (%)
(	1128	95.0%
[	52	4.4%
{	6	0.5%
【	1	0.1%

Dash Punctuation

Value	Count	Frequency (%)
-	139302	> 99.9%
–	9	< 0.1%
—	1	< 0.1%

Space Separator

Value	Count	Frequency (%)
	105	83.3%
	21	16.7%

Nonspacing Mark

Value	Count	Frequency (%)
️	3	50.0%
́	3	50.0%

Other Symbol

Value	Count	Frequency (%)
�	3	75.0%
©	1	25.0%

Connector Punctuation

Value	Count	Frequency (%)
_	46423	100.0%

Initial Punctuation

Value	Count	Frequency (%)
‘	17	100.0%

Modifier Letter

Value	Count	Frequency (%)
ー	3	100.0%

Currency Symbol

Value	Count	Frequency (%)
$	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2869913	63.9%
Common	1618067	36.0%
Cyrillic	474	< 0.1%
Han	52	< 0.1%
Hangul	32	< 0.1%
Katakana	24	< 0.1%
Hebrew	11	< 0.1%
Inherited	6	< 0.1%
Hiragana	5	< 0.1%
Greek	4	< 0.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	252433	8.8%
e	237993	8.3%
s	193173	6.7%
o	182977	6.4%
a	180438	6.3%
p	164093	5.7%
i	159819	5.6%
c	158991	5.5%
n	131682	4.6%
m	119893	4.2%
Other values (46)	1088421	37.9%

Common

Value	Count	Frequency (%)
/	292670	18.1%
0	152961	9.5%
.	141768	8.8%
-	139302	8.6%
2	137747	8.5%
1	129942	8.0%
3	80972	5.0%
4	65328	4.0%
6	64475	4.0%
5	61166	3.8%
Other values (40)	351736	21.7%

Cyrillic

Value	Count	Frequency (%)
е	66	13.9%
а	59	12.4%
н	43	9.1%
в	40	8.4%
с	38	8.0%
р	29	6.1%
и	27	5.7%
о	15	3.2%
т	15	3.2%
ь	14	3.0%
Other values (24)	128	27.0%

Han

Value	Count	Frequency (%)
午	5	9.6%
上	5	9.6%
屏	5	9.6%
截	5	9.6%
星	2	3.8%
合	2	3.8%
速	2	3.8%
本	1	1.9%
副	1	1.9%
大	1	1.9%
Other values (23)	23	44.2%

Hangul

Value	Count	Frequency (%)
프	3	9.4%
에	2	6.2%
로	2	6.2%
티	2	6.2%
아	2	6.2%
셀	1	3.1%
뒤	1	3.1%
일	1	3.1%
독	1	3.1%
도	1	3.1%
Other values (16)	16	50.0%

Hebrew

Value	Count	Frequency (%)
ל	2	18.2%
ס	1	9.1%
ז	1	9.1%
ה	1	9.1%
ק	1	9.1%
ב	1	9.1%
ח	1	9.1%
א	1	9.1%
מ	1	9.1%
ן	1	9.1%

Katakana

Value	Count	Frequency (%)
ョ	3	12.5%
ッ	3	12.5%
ト	3	12.5%
シ	3	12.5%
ン	3	12.5%
リ	3	12.5%
ク	3	12.5%
ス	3	12.5%

Hiragana

Value	Count	Frequency (%)
に	1	20.0%
も	1	20.0%
と	1	20.0%
を	1	20.0%
な	1	20.0%

Greek

Value	Count	Frequency (%)
Τ	1	25.0%
Ε	1	25.0%
Π	1	25.0%
Α	1	25.0%

Inherited

Value	Count	Frequency (%)
️	3	50.0%
́	3	50.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	4487894	> 99.9%
Cyrillic	474	< 0.1%
CJK	52	< 0.1%
Punctuation	50	< 0.1%
None	34	< 0.1%
Hangul	32	< 0.1%
Katakana	27	< 0.1%
Hebrew	11	< 0.1%
Hiragana	5	< 0.1%
VS	3	< 0.1%
Other values (2)	6	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
/	292670	6.5%
t	252433	5.6%
e	237993	5.3%
s	193173	4.3%
o	182977	4.1%
a	180438	4.0%
p	164093	3.7%
i	159819	3.6%
c	158991	3.5%
0	152961	3.4%
Other values (80)	2512346	56.0%

Cyrillic

Value	Count	Frequency (%)
е	66	13.9%
а	59	12.4%
н	43	9.1%
в	40	8.4%
с	38	8.0%
р	29	6.1%
и	27	5.7%
о	15	3.2%
т	15	3.2%
ь	14	3.0%
Other values (24)	128	27.0%

Punctuation

Value	Count	Frequency (%)
	21	42.0%
‘	17	34.0%
–	9	18.0%
…	2	4.0%
—	1	2.0%

None

Value	Count	Frequency (%)
·	14	41.2%
×	7	20.6%
í	2	5.9%
ü	2	5.9%
ú	1	2.9%
【	1	2.9%
Τ	1	2.9%
ä	1	2.9%
Ε	1	2.9%
Π	1	2.9%
Other values (3)	3	8.8%

CJK

Value	Count	Frequency (%)
午	5	9.6%
上	5	9.6%
屏	5	9.6%
截	5	9.6%
星	2	3.8%
合	2	3.8%
速	2	3.8%
本	1	1.9%
副	1	1.9%
大	1	1.9%
Other values (23)	23	44.2%

Katakana

Value	Count	Frequency (%)
ョ	3	11.1%
ッ	3	11.1%
ト	3	11.1%
シ	3	11.1%
ン	3	11.1%
ー	3	11.1%
リ	3	11.1%
ク	3	11.1%
ス	3	11.1%

VS

Value	Count	Frequency (%)
️	3	100.0%

Hangul

Value	Count	Frequency (%)
프	3	9.4%
에	2	6.2%
로	2	6.2%
티	2	6.2%
아	2	6.2%
셀	1	3.1%
뒤	1	3.1%
일	1	3.1%
독	1	3.1%
도	1	3.1%
Other values (16)	16	50.0%

Specials

Value	Count	Frequency (%)
�	3	100.0%

Diacriticals

Value	Count	Frequency (%)
́	3	100.0%

Hebrew

Value	Count	Frequency (%)
ל	2	18.2%
ס	1	9.1%
ז	1	9.1%
ה	1	9.1%
ק	1	9.1%
ב	1	9.1%
ח	1	9.1%
א	1	9.1%
מ	1	9.1%
ן	1	9.1%

Hiragana

Value	Count	Frequency (%)
に	1	20.0%
も	1	20.0%
と	1	20.0%
を	1	20.0%
な	1	20.0%

published_at
Text

Distinct	23427
Distinct (%)	51.4%
Missing	16
Missing (%)	< 0.1%
Memory size	711.8 KiB

Length

Max length	26
Median length	26
Mean length	25.998551
Min length	2

Characters and Unicode

Total characters	1183974
Distinct characters	14
Distinct categories	4 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	15531 ?
Unique (%)	34.1%

Sample

1st row	2023-10-30 10:12:35.000000
2nd row	2023-10-06 04:40:02.000000
3rd row	2023-10-30 10:53:30.000000
4th row	2023-10-06 01:20:24.000000
5th row	2023-10-27 01:08:34.000000

Value	Count	Frequency (%)
2023-11-21	23603	25.9%
2023-11-02	8603	9.4%
2023-11-22	8131	8.9%
00:00:00.000000	1134	1.2%
1970-01-01	834	0.9%
12:00:00.000000	378	0.4%
06:00:00.000000	346	0.4%
14:00:00.000000	340	0.4%
11:00:00.000000	275	0.3%
10:00:00.000000	255	0.3%
Other values (19866)	47180	51.8%

Most occurring characters

Value	Count	Frequency (%)
0	419046	35.4%
1	164791	13.9%
2	163501	13.8%
-	91078	7.7%
:	91078	7.7%
3	68454	5.8%
	45539	3.8%
.	45533	3.8%
4	24796	2.1%
5	23877	2.0%
Other values (4)	46281	3.9%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	910746	76.9%
Other Punctuation	136611	11.5%
Dash Punctuation	91078	7.7%
Space Separator	45539	3.8%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	419046	46.0%
1	164791	18.1%
2	163501	18.0%
3	68454	7.5%
4	24796	2.7%
5	23877	2.6%
6	13223	1.5%
7	12027	1.3%
8	10762	1.2%
9	10269	1.1%

Other Punctuation

Value	Count	Frequency (%)
:	91078	66.7%
.	45533	33.3%

Dash Punctuation

Value	Count	Frequency (%)
-	91078	100.0%

Space Separator

Value	Count	Frequency (%)
	45539	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	1183974	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	419046	35.4%
1	164791	13.9%
2	163501	13.8%
-	91078	7.7%
:	91078	7.7%
3	68454	5.8%
	45539	3.8%
.	45533	3.8%
4	24796	2.1%
5	23877	2.0%
Other values (4)	46281	3.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1183974	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	419046	35.4%
1	164791	13.9%
2	163501	13.8%
-	91078	7.7%
:	91078	7.7%
3	68454	5.8%
	45539	3.8%
.	45533	3.8%
4	24796	2.1%
5	23877	2.0%
Other values (4)	46281	3.9%

content
Text

Distinct	29267
Distinct (%)	64.3%
Missing	35
Missing (%)	0.1%
Memory size	711.8 KiB

Length

Max length	216
Median length	214
Mean length	207.49379
Min length	9

Characters and Unicode

Total characters	9445325
Distinct characters	688
Distinct categories	22 ?
Distinct scripts	9 ?
Distinct blocks	18 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	21260 ?
Unique (%)	46.7%

Sample

1st row	UN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastatin… [+2444 chars]
2nd row	RANDEBOO2023718()WEB2023 Autumn Winter "Nepal Handmade ram vest"Nepal Handmade ram knit"2023106()12:00WEB 106() 12:00 https://x.gd/226uY Nepal Handmade ram vest ¥17,600() Gray/Ivory [] 1 [D… [+583 chars]
3rd row	Kathmandu, Nepal UN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witn… [+2468 chars]
4th row	At least 14 persons lost their lives and more than 100 others, including 23 army personnel, are reportedly missing in Sikkim after the Teesta river went into spate on Wednesday. The flash floods seem… [+1826 chars]
5th row	Scores of foreign citizens were killed, taken hostage or listed as missing after the Hamas terror group launched a major assault on Israel on October 7. More than 1,400, mainly civilians, have been … [+4658 chars]

Value	Count	Frequency (%)
the	63280	4.2%
chars	43564	2.9%
a	31633	2.1%
of	29917	2.0%
to	27057	1.8%
in	26673	1.8%
and	24577	1.6%
	21178	1.4%
on	13640	0.9%
is	12348	0.8%
Other values (114484)	1220424	80.6%

Most occurring characters

Value	Count	Frequency (%)
	1434016	15.2%
e	783484	8.3%
a	613970	6.5%
t	515522	5.5%
i	496113	5.3%
n	489247	5.2%
r	477098	5.1%
o	475593	5.0%
s	460403	4.9%
h	282298	3.0%
Other values (678)	3417581	36.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6556533	69.4%
Space Separator	1442188	15.3%
Uppercase Letter	569060	6.0%
Decimal Number	343819	3.6%
Other Punctuation	238347	2.5%
Control	64316	0.7%
Math Symbol	61806	0.7%
Open Punctuation	60480	0.6%
Close Punctuation	59698	0.6%
Dash Punctuation	33789	0.4%
Other values (12)	15289	0.2%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
の	48	3.1%
で	29	1.9%
す	27	1.8%
が	26	1.7%
ラ	25	1.6%
て	25	1.6%
と	24	1.6%
に	23	1.5%
ン	23	1.5%
ま	22	1.4%
Other values (406)	1264	82.3%

Lowercase Letter

Value	Count	Frequency (%)
e	783484	11.9%
a	613970	9.4%
t	515522	7.9%
i	496113	7.6%
n	489247	7.5%
r	477098	7.3%
o	475593	7.3%
s	460403	7.0%
h	282298	4.3%
l	279998	4.3%
Other values (73)	1682807	25.7%

Uppercase Letter

Value	Count	Frequency (%)
S	48486	8.5%
A	47874	8.4%
T	43658	7.7%
I	35825	6.3%
C	34704	6.1%
E	31870	5.6%
N	31294	5.5%
M	26942	4.7%
R	26626	4.7%
P	26485	4.7%
Other values (44)	215296	37.8%

Other Punctuation

Value	Count	Frequency (%)
,	83181	34.9%
.	61821	25.9%
…	43746	18.4%
:	13069	5.5%
/	9910	4.2%
'	9778	4.1%
"	5987	2.5%
%	2895	1.2%
?	1952	0.8%
;	1750	0.7%
Other values (23)	4258	1.8%

Control

Value	Count	Frequency (%)
	38070	59.2%
	26197	40.7%
	14	< 0.1%
	8	< 0.1%
	6	< 0.1%
	5	< 0.1%
	4	< 0.1%
	3	< 0.1%
	2	< 0.1%
	2	< 0.1%
Other values (4)	5	< 0.1%

Math Symbol

Value	Count	Frequency (%)
+	44795	72.5%
<	7957	12.9%
>	7806	12.6%
\|	828	1.3%
×	196	0.3%
=	120	0.2%
~	75	0.1%
±	14	< 0.1%
→	10	< 0.1%
−	2	< 0.1%
Other values (3)	3	< 0.1%

Decimal Number

Value	Count	Frequency (%)
2	70172	20.4%
1	50719	14.8%
0	49825	14.5%
3	38888	11.3%
4	25729	7.5%
5	25721	7.5%
6	21601	6.3%
9	20944	6.1%
7	20548	6.0%
8	19671	5.7%

Open Punctuation

Value	Count	Frequency (%)
[	45335	75.0%
(	15072	24.9%
{	32	0.1%
„	20	< 0.1%
「	13	< 0.1%
（	5	< 0.1%
【	2	< 0.1%
《	1	< 0.1%

Other Symbol

Value	Count	Frequency (%)
©	393	65.0%
®	167	27.6%
°	36	6.0%
™	4	0.7%
♥	2	0.3%
❗	1	0.2%
▪	1	0.2%
⚠	1	0.2%

Close Punctuation

Value	Count	Frequency (%)
]	45301	75.9%
)	14353	24.0%
}	26	< 0.1%
」	11	< 0.1%
）	4	< 0.1%
】	2	< 0.1%
》	1	< 0.1%

Format

Value	Count	Frequency (%)
‍	43	48.9%
	23	26.1%
‌	18	20.5%
	2	2.3%
⁣	1	1.1%
‎	1	1.1%

Currency Symbol

Value	Count	Frequency (%)
$	3036	93.6%
£	107	3.3%
¥	64	2.0%
€	32	1.0%
¢	6	0.2%

Modifier Symbol

Value	Count	Frequency (%)
^	163	86.2%
`	11	5.8%
¨	5	2.6%
´	5	2.6%
¯	5	2.6%

Other Number

Value	Count	Frequency (%)
²	25	53.2%
³	16	34.0%
½	4	8.5%
¾	1	2.1%
¹	1	2.1%

Space Separator

Value	Count	Frequency (%)
	1434016	99.4%
	8165	0.6%
	6	< 0.1%
	1	< 0.1%

Dash Punctuation

Value	Count	Frequency (%)
-	31182	92.3%
–	2085	6.2%
—	520	1.5%
‑	2	< 0.1%

Final Punctuation

Value	Count	Frequency (%)
’	4205	65.7%
”	1274	19.9%
»	915	14.3%
›	4	0.1%

Initial Punctuation

Value	Count	Frequency (%)
“	1603	56.8%
«	995	35.3%
‘	223	7.9%

Modifier Letter

Value	Count	Frequency (%)
ー	50	98.0%
々	1	2.0%

Connector Punctuation

Value	Count	Frequency (%)
_	304	100.0%

Letter Number

Value	Count	Frequency (%)
ⅽ	3	100.0%

Nonspacing Mark

Value	Count	Frequency (%)
️	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	7125612	75.4%
Common	2318132	24.5%
Han	589	< 0.1%
Hiragana	469	< 0.1%
Katakana	355	< 0.1%
Arabic	90	< 0.1%
Inherited	63	< 0.1%
Cyrillic	12	< 0.1%
Greek	3	< 0.1%

Most frequent character per script

Han

Value	Count	Frequency (%)
品	14	2.4%
電	11	1.9%
製	11	1.9%
下	9	1.5%
用	9	1.5%
先	8	1.4%
開	8	1.4%
多	7	1.2%
特	7	1.2%
販	7	1.2%
Other values (268)	498	84.6%

Latin

Value	Count	Frequency (%)
e	783484	11.0%
a	613970	8.6%
t	515522	7.2%
i	496113	7.0%
n	489247	6.9%
r	477098	6.7%
o	475593	6.7%
s	460403	6.5%
h	282298	4.0%
l	279998	3.9%
Other values (126)	2251886	31.6%

Common

Value	Count	Frequency (%)
	1434016	61.9%
,	83181	3.6%
2	70172	3.0%
.	61821	2.7%
1	50719	2.2%
0	49825	2.1%
[	45335	2.0%
]	45301	2.0%
+	44795	1.9%
…	43746	1.9%
Other values (121)	389221	16.8%

Katakana

Value	Count	Frequency (%)
ラ	25	7.0%
ン	23	6.5%
ッ	20	5.6%
ク	19	5.4%
デ	19	5.4%
イ	16	4.5%
ス	16	4.5%
ル	16	4.5%
フ	13	3.7%
ム	12	3.4%
Other values (55)	176	49.6%

Hiragana

Value	Count	Frequency (%)
の	48	10.2%
で	29	6.2%
す	27	5.8%
が	26	5.5%
て	25	5.3%
と	24	5.1%
に	23	4.9%
ま	22	4.7%
い	22	4.7%
し	21	4.5%
Other values (38)	202	43.1%

Arabic

Value	Count	Frequency (%)
ا	14	15.6%
ی	14	15.6%
ک	8	8.9%
ر	5	5.6%
ن	4	4.4%
م	4	4.4%
س	4	4.4%
و	4	4.4%
ں	3	3.3%
ت	3	3.3%
Other values (14)	27	30.0%

Inherited

Value	Count	Frequency (%)
‍	43	68.3%
‌	18	28.6%
️	2	3.2%

Greek

Value	Count	Frequency (%)
β	2	66.7%
α	1	33.3%

Cyrillic

Value	Count	Frequency (%)
е	12	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9355125	99.0%
Punctuation	53770	0.6%
None	34796	0.4%
CJK	588	< 0.1%
Hiragana	469	< 0.1%
Katakana	413	< 0.1%
Arabic	90	< 0.1%
Currency Symbols	32	< 0.1%
Cyrillic	12	< 0.1%
Arrows	12	< 0.1%
Other values (8)	18	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1434016	15.3%
e	783484	8.4%
a	613970	6.6%
t	515522	5.5%
i	496113	5.3%
n	489247	5.2%
r	477098	5.1%
o	475593	5.1%
s	460403	4.9%
h	282298	3.0%
Other values (87)	3327381	35.6%

Punctuation

Value	Count	Frequency (%)
…	43746	81.4%
’	4205	7.8%
–	2085	3.9%
“	1603	3.0%
”	1274	2.4%
—	520	1.0%
‘	223	0.4%
‍	43	0.1%
„	20	< 0.1%
‌	18	< 0.1%
Other values (11)	33	0.1%

None

Value	Count	Frequency (%)
	8165	23.5%
é	5060	14.5%
ó	2113	6.1%
á	2041	5.9%
ä	1871	5.4%
ü	1549	4.5%
í	1460	4.2%
ö	1060	3.0%
à	996	2.9%
«	995	2.9%
Other values (130)	9486	27.3%

Katakana

Value	Count	Frequency (%)
ー	50	12.1%
ラ	25	6.1%
ン	23	5.6%
ッ	20	4.8%
ク	19	4.6%
デ	19	4.6%
イ	16	3.9%
ス	16	3.9%
ル	16	3.9%
フ	13	3.1%
Other values (57)	196	47.5%

Hiragana

Value	Count	Frequency (%)
の	48	10.2%
で	29	6.2%
す	27	5.8%
が	26	5.5%
て	25	5.3%
と	24	5.1%
に	23	4.9%
ま	22	4.7%
い	22	4.7%
し	21	4.5%
Other values (38)	202	43.1%

Currency Symbols

Value	Count	Frequency (%)
€	32	100.0%

Arabic

Value	Count	Frequency (%)
ا	14	15.6%
ی	14	15.6%
ک	8	8.9%
ر	5	5.6%
ن	4	4.4%
م	4	4.4%
س	4	4.4%
و	4	4.4%
ں	3	3.3%
ت	3	3.3%
Other values (14)	27	30.0%

CJK

Value	Count	Frequency (%)
品	14	2.4%
電	11	1.9%
製	11	1.9%
下	9	1.5%
用	9	1.5%
先	8	1.4%
開	8	1.4%
多	7	1.2%
特	7	1.2%
販	7	1.2%
Other values (267)	497	84.5%

Cyrillic

Value	Count	Frequency (%)
е	12	100.0%

Arrows

Value	Count	Frequency (%)
→	10	83.3%
↑	1	8.3%
⇒	1	8.3%

Letterlike Symbols

Value	Count	Frequency (%)
™	4	100.0%

Number Forms

Value	Count	Frequency (%)
ⅽ	3	100.0%

Math Operators

Value	Count	Frequency (%)
−	2	100.0%

Misc Symbols

Value	Count	Frequency (%)
♥	2	66.7%
⚠	1	33.3%

VS

Value	Count	Frequency (%)
️	2	100.0%

Latin Ext Additional

Value	Count	Frequency (%)
ầ	2	100.0%

Dingbats

Value	Count	Frequency (%)
❗	1	100.0%

Geometric Shapes

Value	Count	Frequency (%)
▪	1	100.0%

category
Text

Distinct	157
Distinct (%)	0.3%
Missing	30
Missing (%)	0.1%
Memory size	711.8 KiB

Length

Max length	37
Median length	20
Mean length	7.319356
Min length	3

Characters and Unicode

Total characters	333221
Distinct characters	52
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	8 ?
Unique (%)	< 0.1%

Sample

1st row	Nepal
2nd row	Nepal
3rd row	Nepal
4th row	Nepal
5th row	Nepal

Value	Count	Frequency (%)
love	1460	2.9%
food	1457	2.9%
africa	1084	2.2%
home	999	2.0%
politics	986	2.0%
climate	961	1.9%
games	942	1.9%
space	864	1.7%
stock	841	1.7%
real	831	1.7%
Other values (162)	39660	79.2%

Most occurring characters

Value	Count	Frequency (%)
a	35042	10.5%
e	30230	9.1%
i	28489	8.5%
o	21469	6.4%
t	21377	6.4%
n	20226	6.1%
r	18990	5.7%
l	14632	4.4%
s	14013	4.2%
c	13636	4.1%
Other values (42)	115117	34.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	277452	83.3%
Uppercase Letter	51109	15.3%
Space Separator	4559	1.4%
Other Punctuation	101	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	35042	12.6%
e	30230	10.9%
i	28489	10.3%
o	21469	7.7%
t	21377	7.7%
n	20226	7.3%
r	18990	6.8%
l	14632	5.3%
s	14013	5.1%
c	13636	4.9%
Other values (15)	59348	21.4%

Uppercase Letter

Value	Count	Frequency (%)
A	6565	12.8%
S	5011	9.8%
P	4636	9.1%
C	3958	7.7%
I	3733	7.3%
F	2988	5.8%
H	2670	5.2%
M	2135	4.2%
N	2051	4.0%
G	1965	3.8%
Other values (15)	15397	30.1%

Space Separator

Value	Count	Frequency (%)
	4559	100.0%

Other Punctuation

Value	Count	Frequency (%)
,	101	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	328561	98.6%
Common	4660	1.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	35042	10.7%
e	30230	9.2%
i	28489	8.7%
o	21469	6.5%
t	21377	6.5%
n	20226	6.2%
r	18990	5.8%
l	14632	4.5%
s	14013	4.3%
c	13636	4.2%
Other values (40)	110457	33.6%

Common

Value	Count	Frequency (%)
	4559	97.8%
,	101	2.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	333221	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	35042	10.5%
e	30230	9.1%
i	28489	8.5%
o	21469	6.4%
t	21377	6.4%
n	20226	6.1%
r	18990	5.7%
l	14632	4.4%
s	14013	4.2%
c	13636	4.1%
Other values (42)	115117	34.5%

full_content
Text

MISSING

Distinct	2509
Distinct (%)	97.6%
Missing	42986
Missing (%)	94.4%
Memory size	711.8 KiB

Length

Max length	177956
Median length	5081.5
Mean length	6733.6751
Min length	1

Characters and Unicode

Total characters	17305545
Distinct characters	847
Distinct categories	24 ?
Distinct scripts	10 ?
Distinct blocks	27 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2451 ?
Unique (%)	95.4%

Sample

1st row	UN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastating impact of the phenomenon. "The rooftops of the world are caving in," Guterres said on a visit to the Everest region in mountainous Nepal, adding that the country had lost nearly a third of its ice in just over three decades. "Glaciers are icy reservoirs -- the ones here in the Himalayas supply fresh water to well over a billion people," he said. "When they shrink, so do river flows." Nepal's glaciers melted 65 percent faster in the last decade than in the previous one, said Guterres, who is on a four-day visit to Nepal. Glaciers in the wider Himalayan and Hindu Kush ranges are a crucial water source for around 240 million people in the mountainous regions, as well as for another 1.65 billion people in the South Asian and Southeast Asian river valleys below. The glaciers feed 10 of the world's most important river systems, including the Ganges, Indus, Yellow, Mekong and Irrawaddy, and directly or indirectly supply billions of people with food, energy, clean air and income. Scientists say they are melting faster than ever before due to climate change, exposing communities to unpredictable and costly disasters. "I am here today to cry out from the rooftop of the world: stop the madness", Guterres said, speaking from Syangboche village, with the icy peak of the world's highest mountain Everest towering behind him. "The glaciers are retreating, but we cannot. We must end the fossil fuel age," he said. The world has warmed an average of nearly 1.2 degrees Celsius since the mid-1800s, unleashing a cascade of extreme weather, including more intense heatwaves, more severe droughts and storms made more ferocious by rising seas. Hardest hit are the most vulnerable people and the world's poorest countries, which have done little to contribute to the fossil fuel emissions that drive up temperatures. "We must act now to protect people on the frontline, and to limit global temperature rise to 1.5 degrees, to avert the worst of climate chaos," Guterres said. "The world can't wait." In the first phase of climate change's effects, melting glaciers can trigger destructive floods. "Melting glaciers mean swollen lakes and rivers flooding, sweeping away entire communities", he added. But all too soon, glaciers will dry up if change is not made, he warned. "In the future, major Himalayan rivers like the Indus, the Ganges and Brahmaputra could have massively reduced flows, he said. "That spells catastrophe".
2nd row	At least 14 persons lost their lives and more than 100 others, including 23 army personnel, are reportedly missing in Sikkim after the Teesta river went into spate on Wednesday. The flash floods seem to have been triggered by a combination of factors. A cloud burst ripped apart the South Shonak Lake — a glacial body in the state’s northwest. According to Sikkim’s Disaster Management Authority (SDMA), the Teesta has inundated at least four districts in the state. The calamity was aggravated by the release of water from the Chungthang dam — initial reports suggest that the breach was caused by water rushing from the mountains. Disaster management authorities are also investigating the possibility of the event being triggered by an earthquake in Nepal on Tuesday. The probe is likely to throw more light on the immediate causes of the flash flood but one thing has long been clear — states in the Himalayan region must respect the fragile ecology of the mountains and put in adequate safeguards to mitigate the damage caused by increasingly frequent extreme rainfall events. For years, studies have red-flagged the South Shonak Lake’s expansion due to glacial melting and warned that the water body is susceptible to breaches. In 2021, for instance, a study by scientists from IIT Roorkee, Indian Institute of ScienceBangalore, University of Dayton, USA, University of Graz, Austria, and the Universities of Zurich and Geneva in Switzerland recommended regular monitoring of the lake’s growth and continuous assessment of the region’s slope stability. The National Disaster Management Authority guidelines also say that risk reduction has to begin with mapping such water bodies, taking structural measures to prevent their breach and establishing mechanisms that can alert people about glacial lake outbursts. The IMD has collaborated with the US National Weather Service to warn people about six to 24 hours before a flash flood. But the system doesn’t seem to have come to terms with the Himalayan region’s idiosyncrasies. The Northeast has a key place in the hydel power push of successive governments at the Centre. The Chungthang Dam is a part of the 1,200 MW Teesta Stage 3 Hydroelectric Project. The government claims that such projects are climate-friendly because of their low emissions intensity. Hydroelectric power is also a major source of revenue for Sikkim. Ecologists, however, caution against the adverse effects of dam construction — it increases the volatility of rocks in the Himalayan region. Wednesday’s disaster is a warning to take such caveats seriously and install robust safety mechanisms.
3rd row	India, the first non-Arab country to recognise the PLO in the 1970s, is now seen closer to Israel and its biggest benefactor, the United States. New Delhi, India– Israel’srelentless bombingof the besieged Gaza Strip and killing of nearly 6,000 people – a third of them children – in two weeks has outraged people across the world, triggering mass protests and a call for an immediate ceasefire. However, in India – the first non-Arab country to recognise the Palestine Liberation Organization (PLO), but now seen closer to Israel and its biggest benefactor, the United States – some pro-Palestine protesters reported being targeted by the government. Less than a week after the Gaza assault began, police in Hamirpur district of India’s most populous Uttar Pradesh state were looking for Muslim scholars Atif Chaudhary and Suhail Ansari. Their alleged crime: putting a WhatsApp display photo that said: “I stand with Palestine.” The two men were charged with promoting enmity between social groups. Ansari is under arrest, while Chaudhary is on the run, according to the police. In the same state, governed by the Hindu nationalist Bharatiya Janata Party (BJP), four students of the Aligarh Muslim University were booked by the police after they took out a pro-Palestine march on the campus a day after the Gaza assault began on October 7. However, when the Hindu far-right group Bajrang Dal took out a pro-Israel march in the same Aligarh city, raising slogans such as “Down with Palestine, Down with Hamas”, no action was taken against them by the authorities. In the national capital, New Delhi, there have been several examples of people being detained during rallies organised by student groups, activists and citizens for solidarity with the Palestinians since October 7. In the western state of Maharashtra, also governed by the BJP in alliance with a regional party, two protesters, Ruchir Lad and Supreeth Ravish, were arrested on October 13 for holding a march against the war on Gaza and charged with unlawful assembly. Pooja Chinchole, member of the Revolutionary Workers Party of India and one of the organisers of the protest held in state capital Mumbai, told Al Jazeera the police “created many hurdles before us when they got to know that we are organising a pro-Palestine protest”. “They detained one of the organisers a day before the protest and three organisers on the morning of the protest. When we still gathered to protest, they snatched our microphone, placards, and after a while, started using force on some of us,” she said. The crackdown, however, was not limited to the BJP-ruled states only. In the southern Karnataka state, governed by the main opposition Congress party, police charged 10 activists with creating a public nuisance after they organised a silent march in support of the Palestinians on October 16 in Bengaluru, the capital of the state. The Karnataka police also arrested a 58-year-old Muslim man for allegedly posting a video in support of Hamas on WhatsApp. Police also briefly detained Alam Nawaz, a Muslim government employee, for updating his WhatsApp status with a Palestinian flag and “Long Live Palestine” message. “People started seeing me with suspicion as if I have committed some crime by expressing my solidarity with Palestinian people,” Nawaz, 20, told Al Jazeera. All this despite the Congress expressing its support for the “rights of the Palestinian people to land, self-government and to live with dignity” as the party called for an immediate ceasefire in a resolution passed by its working committee on October 9. Meanwhile, pro-Israel rallies, organised mainly byHindu right-wing groups, were seen across India, while many on social media offered their services to the Israeli forces. On Saturday, dozens of supporters of a retired Indian army soldier travelled 182km (113 miles) to reach the Israeli embassy in New Delhi where they offered to go to Israel to fight against the Palestinians in Gaza. Last week, one of India’s most influential Hindu nationalists, Yati Narsinghanand, released a video in which he said Hindus and Jews “have the same enemy: Muhammad and his satanic book” as he urged the Israeli government to allow 1,000 Hindus to settle in Israel in order to “take on those Muslims”. Israel’s ambassador to India, Naor Gilon, on October 8 said he had received several requests from Indians wanting to voluntarily fight for Israel. Apoorvanand, professor of Hindi language at Delhi University, told Al Jazeera he was not surprised that the Hindu far right, which openly admires Adolf Hitler for his action against the Jews, is now supporting the Zionists in Israel. “Hindu far-right organisations in India have always supported those who dominate by violence. Hitler did once, so they supported him. Now Israel is doing this, so they are supporting it,” he said. Apoorvanand said the Hindu right in India thinks there are ideological linkages between them and the Zionists in Israel. “It looks like Israel is fighting a proxy war on behalf of the Hindu far right. They think Israel is fighting and decimating Muslims on their behalf. The way they want to establish Akhand Bharat [Unified India] by joining Pakistan, Afghanistan, Nepal together with India, they think Israel is following the same expansionist ideology,” he said. This was not always the case. India’s foreign policy has historically supported the Palestinian cause, which began with India voting against the United Nations resolution to create the state of Israel in 1947 and then recognising the PLO as a representative of the Palestinian people in 1974. India’s pro-Palestine stand was guided by the shared history of colonisation by the British, Zikrur Rahman, former Indian ambassador to Palestine, told Al Jazeera. “In the postcolonial era, we identified that this is a colonial attempt to divide the country and to create another country. We were not in favour of the creation of a country on the basis of religion,” he said. Rahman, however, added that while India’s position on Palestine has not changed, it is not as strong as it used to be. India recognised the creation of Israel in 1950, but did not establish diplomatic relations until 1992, when the details of the firstOslo Accordwere being finalised. Since then, India has tried to strike a balance between its strategic relations with Israel and sympathising with the Palestinian struggle. Today, India is the largest buyer of Israeli-made weapons, while strategic and security cooperation between them has grown manifold. Comparisons have also been made between Israel demolishing homes of Palestinians in the occupied territories and a similar policy adopted by some BJP state governmentsmainly against Muslimsas forms of “collective punishment” of the community. Since Prime Minister Narendra Modi came to power in 2014, he has made public statements, calling his Israeli counterpart Benjamin Netanyahu a “good friend” on several occasions. Modi was one of the first global leaders to post his solidarity with Israel after Hamas’s unprecedented incursion on October 7. “Deeply shocked by the news of terrorist attacks in Israel,” said his post on X, which came four hours before US President Joe Biden reacted to the event. Modi also condemned the Israeli attack onal-Ahli Arab Hospitalin Gaza on October 18, in which nearly 500 Palestinians were killed, though his message on X appeared nearly eight hours after Biden’s post. Meanwhile, India’s Ministry of External Affairs issued a statement on October 12, reiterating New Delhi’s position of establishing a “sovereign, independent, and viable state of Palestine, living within secure and recognised borders, side by side at peace with Israel”. Last week, Modi posted on X about his phone call with the Palestinian Authority President Mahmoud Abbas, in which he repeated India’s “longstanding principled position on the Israel-Palestine issue”. He said his government is sending humanitarian assistance for the besieged residents of Gaza. Journalist Anand K Sahay, however, thinks India’s response to the unfolding humanitarian disaster in Gaza has not been adequate. “What India didn’t say is important. India didn’t demand a ceasefire. Historically, India has always demanded a ceasefire in case of a [foreign] war. In this case also we should have strongly said: stop the war,” he told Al Jazeera. Sahay said Modi’s flaunting of closeness with Israel is also aimed at appeasing his core vote bank: the Hindus. “Suppose there was another religion in majority in Palestine. Then our stand may have been different. During the Russia-Ukraine war, we said ‘this is not an age of war’. Why couldn’t we say this in case of Israel-Palestine war?” asked Sahay. “By not asking for a ceasefire, India was also indirectly signalling the US that the Indian position was very close to the US line.” Follow Al Jazeera English:
4th row	Written by Alex Travelli and Hari Kumar No nation in the world is buying as many airplanes as India. Its largest airlines have ordered nearly 1,000 jets this year, committing tens of billions of dollars to a spending spree that is unparalleled in aviation. In NewDelhi, Indira Gandhi International Airport will be ready for 109 million passengers next year, as it prepares to become the world’s second busiest, behind Hartsfield-Jackson Atlanta International Airport in the United States. And this is happening in a vast country still heavily reliant on trains — with 20 journeys by rail for every one by air. The enormous aviation build-out, with a surge of investment behind it, has pride of place in India’s case for a greater standing on the world stage. As it moves up the ranks of the world’s biggest economies, India is scrambling to meet the expanding ambitions of its ascendant middle class. Its airports present highly visible achievements. Air travel remains out of the financial reach of most Indians. An estimated 3% of the country’s population flies on a regular basis. But in a nation of 1.4 billion people, that percentage represents 42 million — executives, students and engineers who yearn to get quickly from here to there inside India’s borders, and to gain easier access to destinations beyond, for both business and vacation. Kapil Kaul, CEO of CAPA India, an advisory firm focused on aviation, calls “the next two to three years critical for achieving the quality of growth that India desires and deserves.” Growth has so far been profitless. Now Indian aviation must prove it can make money. The effects of the spending spree should redound across India’s economy. Cargo comes with passenger traffic, and foreign investment tends to follow closely behind, Kaul said. Arrivals to the international terminal at Indira Gandhi Airport are greeted by a wall of giant sculptural hands, their fingers and palms folded into the signifying shapes of the Buddha’s gestures, looking both ancient and futuristic. In 2012, when they were installed, 30 million passengers passed through the airport. By the time the airport has expanded to its new capacity, another one will have been built from scratch on the other side of the city. Indira Gandhi Airport is racing to get bigger. In July it added a fourth runway and opened an elevated taxiway. The company that operates it, GMR Airports, took over in 2006, a time when all arrivals walked past cows lazing in the dust to reach a taxi stand. By 2018 the facility was rated as India’s most valuable infrastructural asset. To spare the use of jet fuel, a battery-powered TaxiBot lugs idling planes around the tarmac. An automated luggage-handling system can sort 6,000 bags an hour. Two beneficiaries of India’s expanding aviation market are the world’s largest airplane makers: Boeing in America and Airbus in Europe. In February, Air India, which Tata Group took private last year, agreed to buy 250 planes from Airbus and 220 from Boeing, worth a combined $70 billion. In June, IndiGo, the country’s biggest carrier by passengers and flights, ordered 500 new Airbus A230s. The bulk of the growth of Indian aviation has been among homegrown airlines, which have clocked a 36% increase in passengers since 2022. Foreign tourist arrivals are rebounding since the pandemic, but are still relatively scarce, barely topping 10 million in a good year (about the same as Romania). So low-cost carriers are adding new countries to their destinations in order to accommodate India’s demand for foreign tourism. Azerbaijan, Kenya and Vietnam are all a direct flight from Delhi orMumbai, India’s financial capital, for less than 21,000 rupees ($250) one way. The air corridor between Delhi and Mumbai was already one of the world’s 10 busiest. Like Delhi, Mumbai has new airport terminals that would be the envy of any city in America, not to mention the glorious new all-bamboo Terminal 2 at Kempegowda International Airport in Bengaluru, a city in southern India. But the expansion in infrastructure is not limited to the country’s premier metropolitan areas. Prime MinisterNarendra Modi’s government likes to point out that the number of airports has doubled in the nine years since he took office, to 148 from 74. Jyotiraditya Scindia, Modi’s aviation minister, said there would be at least 230 by 2030. The government has invested more than $11 billion in airports over the past decade, and Scindia has promised another $15 billion. That means that sleepy towns such asDarbhanga, a former principality in the impoverished state of Bihar in eastern India, now have nonstop access to Delhi, Bengaluru and beyond. For many of the 900 travelers a day who fill its flights, including plenty from nearby Nepal, the new airport has transformed the journey. Prasanna Kumar Jha, 52, was born in Darbhanga but works in Delhi as a tax consultant. “Who ever expected that Darbhanga would be on the air map?” he asked. Flying to his hometown on short notice to see his ailing mother cost him 10,500 rupees ($126), which pinched. “But if you calculate the alternative — by train from Delhi and then taxi to Darbhanga — it will take at least 30 hours,” he said. “The plane journey is no longer a luxury but a necessity.” Darbhanga’s airport is a far cry from New Delhi’s. There is no parking lot. Passengers walk from the edge of a highway past a checkpoint to wait on benches outside the terminal. Then they wait on another set of outdoor benches after clearing the security check. But it works. Another passenger on the same flight at Darbhanga, Ajay Jha, was cradling his 1-year-old daughter, Saranya, as he stood near the rudimentary baggage claim. His family was on the last leg of a trip that started in Bellevue, Washington, where he works as an engineer for Amazon, to a family reunion in the Bihari countryside. Traveling halfway around the world took less time than Jha used to spend getting home from his school in Bengaluru. Yet a vast majority of Indians cannot afford such conveniences. The annual mean income is still less than a single economy-class fare from the United States, and, in this top-heavy economy, most Indians earn much less than that. Middle class, in Indian parlance, indicates somewhere close to the top of the pyramid. A report by CAPA India counted just 0.13 passenger seats per capita in 2019 for Indians, compared with 0.52 for Chinese and 3.03 for Americans. But aviation companies and India’s elected officials look at the low penetration and see opportunity. A scarcity of competition, in the face of an emerging duopoly between IndiGo and the Tata-led airlines, is one of the new landscape’s most striking features. Smaller competitors keep going bust, most recently Go First, which declared bankruptcy in May. A shortage of pilots, after dozens were poached by bigger companies, forced Akasa Air, a promising upstart, to cancel flights in August. But supply shortages are not the worst kind of problem to have in today’s global economy. With aviation’s growth in the decade before the pandemic steady at about 15% a year, the Indian boom seems all but guaranteed to change the future of aviation worldwide. If the benefits accruing to the winners in India’s economy can be coaxed into trickling outward and downward, the same could go for many other sectors.
5th row	NEW DELHI: India preferred Bangladesh over Nepal for the post of regional director of World Health Organisation’s (WHO) Southeast Asia Region contributing to the comfortable victory of Saima Wazed, daughter of Bangla PM Sheikh Hasina .Although officials were tight-lipped about which way India voted, sources said that it voted for Wazed at the expense of Dr Shambhu Acharya, who could manage one vote, apart from Nepal’s.It could not have been an easy decision as India has been working to improve ties with both neighbours. The decision may have been influenced by Bangladesh PM Sheikh Hasina’s effort to stop Islamic terrorist groups, many of them backed by Pakistan, from using Bangladesh as a base to launch attacks against India. The personal stake of Sheikh Hasina, who faces an increasingly tough situation on the domestic front ahead of elections next year, in the outcome of a contest featuring her daughter and China’s effort to woo her, also perhaps helped tip scales in Wazed’s favour.Wazed, who is a global autism advocate and has been a member of the WHO’s 25-member expert advisory panel on mental health, would have won in any case as eight of the 10 member nations preferred her over Dr Acharya who is a WHO specialist.WHO said that Wazed’s nomination will be submitted to the WHO executive board during its 154th session, taking place on 22-27 January 2024 in Geneva, Switzerland, and that the newly appointed regional director will take office on 1 February 2024.The Bangladesh High Commission, in its statement announcing the victory, said their ministry of foreign affairs, in coordination with the ministry of health and family welfare, conducted an effective campaign in Wazed’s favour since the very beginning of the election process.

Value	Count	Frequency (%)
the	134906	5.1%
and	78702	3.0%
of	77640	3.0%
to	64715	2.5%
in	54705	2.1%
a	47298	1.8%
for	26778	1.0%
on	23866	0.9%
	23170	0.9%
is	22114	0.8%
Other values (112282)	2075791	78.9%

Most occurring characters

Value	Count	Frequency (%)
	2697010	15.6%
e	1573260	9.1%
t	1132630	6.5%
a	1088891	6.3%
n	993497	5.7%
i	992605	5.7%
o	967681	5.6%
r	886985	5.1%
s	873179	5.0%
l	516278	3.0%
Other values (837)	5583529	32.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	12766346	73.8%
Space Separator	2723286	15.7%
Uppercase Letter	671799	3.9%
Decimal Number	473815	2.7%
Other Punctuation	445115	2.6%
Dash Punctuation	58446	0.3%
Control	41412	0.2%
Close Punctuation	28330	0.2%
Open Punctuation	28194	0.2%
Final Punctuation	25109	0.1%
Other values (14)	43693	0.3%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
ﺍ	131	4.6%
ﻟ	82	2.9%
の	81	2.9%
ﺎ	61	2.2%
る	47	1.7%
ﻮ	39	1.4%
ﺮ	39	1.4%
ﺔ	38	1.3%
ン	37	1.3%
ﻴ	37	1.3%
Other values (494)	2230	79.0%

Lowercase Letter

Value	Count	Frequency (%)
e	1573260	12.3%
t	1132630	8.9%
a	1088891	8.5%
n	993497	7.8%
i	992605	7.8%
o	967681	7.6%
r	886985	6.9%
s	873179	6.8%
l	516278	4.0%
d	494661	3.9%
Other values (69)	3246679	25.4%

Other Symbol

Value	Count	Frequency (%)
©	463	27.6%
®	429	25.6%
△	311	18.5%
°	168	10.0%
™	124	7.4%
▪	25	1.5%
▲	19	1.1%
Ⓡ	10	0.6%
🇵	9	0.5%
■	7	0.4%
Other values (49)	112	6.7%

Uppercase Letter

Value	Count	Frequency (%)
A	62769	9.3%
T	59000	8.8%
S	58241	8.7%
C	50160	7.5%
I	49595	7.4%
P	37232	5.5%
E	35213	5.2%
M	31281	4.7%
N	28874	4.3%
R	27432	4.1%
Other values (36)	232002	34.5%

Other Punctuation

Value	Count	Frequency (%)
,	179250	40.3%
.	175449	39.4%
"	19045	4.3%
'	16072	3.6%
:	15384	3.5%
%	14729	3.3%
/	11449	2.6%
;	4913	1.1%
&	3402	0.8%
?	2290	0.5%
Other values (25)	3132	0.7%

Math Symbol

Value	Count	Frequency (%)
+	1274	68.0%
\|	273	14.6%
=	116	6.2%
~	89	4.7%
＋	39	2.1%
>	34	1.8%
≤	11	0.6%
<	10	0.5%
◼	6	0.3%
−	5	0.3%
Other values (9)	17	0.9%

Decimal Number

Value	Count	Frequency (%)
2	89887	19.0%
0	88529	18.7%
1	66284	14.0%
3	53690	11.3%
4	33593	7.1%
5	33495	7.1%
9	27557	5.8%
6	27482	5.8%
7	26848	5.7%
8	26434	5.6%
Other values (7)	16	< 0.1%

Close Punctuation

Value	Count	Frequency (%)
)	27648	97.6%
]	612	2.2%
）	49	0.2%
⁾	10	< 0.1%
」	3	< 0.1%
}	3	< 0.1%
〕	3	< 0.1%
】	2	< 0.1%

Open Punctuation

Value	Count	Frequency (%)
(	27517	97.6%
[	612	2.2%
（	48	0.2%
„	6	< 0.1%
「	3	< 0.1%
{	3	< 0.1%
〔	3	< 0.1%
【	2	< 0.1%

Private Use

Value	Count	Frequency (%)
	42	41.6%
	20	19.8%
	18	17.8%
	9	8.9%
	6	5.9%
	4	4.0%
	1	1.0%
	1	1.0%

Other Number

Value	Count	Frequency (%)
₂	10	25.6%
²	9	23.1%
¹	7	17.9%
½	5	12.8%
①	3	7.7%
③	2	5.1%
②	2	5.1%
④	1	2.6%

Dash Punctuation

Value	Count	Frequency (%)
-	50599	86.6%
—	5637	9.6%
–	2131	3.6%
－	34	0.1%
‐	22	< 0.1%
‑	12	< 0.1%
―	11	< 0.1%

Currency Symbol

Value	Count	Frequency (%)
$	24304	97.0%
€	423	1.7%
¥	165	0.7%
£	146	0.6%
₹	21	0.1%
¢	8	< 0.1%
＄	1	< 0.1%

Letter Number

Value	Count	Frequency (%)
Ⅰ	5	22.7%
Ⅱ	5	22.7%
Ⅲ	4	18.2%
Ⅳ	2	9.1%
Ⅵ	2	9.1%
Ⅶ	2	9.1%
Ⅴ	2	9.1%

Space Separator

Value	Count	Frequency (%)
	2697010	99.0%
	26172	1.0%
	57	< 0.1%
	24	< 0.1%
	15	< 0.1%
	8	< 0.1%

Modifier Symbol

Value	Count	Frequency (%)
^	15	50.0%
´	4	13.3%
🏽	4	13.3%
🏻	4	13.3%
🏼	2	6.7%
`	1	3.3%

Final Punctuation

Value	Count	Frequency (%)
’	15456	61.6%
”	9496	37.8%
»	138	0.5%
›	19	0.1%

Modifier Letter

Value	Count	Frequency (%)
ー	54	94.7%
ˈ	1	1.8%
々	1	1.8%
ː	1	1.8%

Nonspacing Mark

Value	Count	Frequency (%)
️	8	61.5%
̈	2	15.4%
̱	2	15.4%
́	1	7.7%

Control

Value	Count	Frequency (%)
	37081	89.5%
	4330	10.5%
	1	< 0.1%

Initial Punctuation

Value	Count	Frequency (%)
“	9825	95.8%
‘	426	4.2%
«	9	0.1%

Format

Value	Count	Frequency (%)
	30	71.4%
	6	14.3%
‏	6	14.3%

Connector Punctuation

Value	Count	Frequency (%)
_	1687	100.0%

Enclosing Mark

Value	Count	Frequency (%)
⃣	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	13438143	77.7%
Common	3864441	22.3%
Arabic	1110	< 0.1%
Hiragana	593	< 0.1%
Han	590	< 0.1%
Katakana	498	< 0.1%
Unknown	101	< 0.1%
Hangul	30	< 0.1%
Greek	25	< 0.1%
Inherited	14	< 0.1%

Most frequent character per script

Han

Value	Count	Frequency (%)
的	11	1.9%
業	10	1.7%
述	10	1.7%
記	10	1.7%
社	10	1.7%
会	9	1.5%
能	9	1.5%
関	9	1.5%
中	9	1.5%
来	8	1.4%
Other values (258)	495	83.9%

Common

Value	Count	Frequency (%)
	2697010	69.8%
,	179250	4.6%
.	175449	4.5%
2	89887	2.3%
0	88529	2.3%
1	66284	1.7%
3	53690	1.4%
-	50599	1.3%
	37081	1.0%
4	33593	0.9%
Other values (188)	393069	10.2%

Latin

Value	Count	Frequency (%)
e	1573260	11.7%
t	1132630	8.4%
a	1088891	8.1%
n	993497	7.4%
i	992605	7.4%
o	967681	7.2%
r	886985	6.6%
s	873179	6.5%
l	516278	3.8%
d	494661	3.7%
Other values (114)	3918476	29.2%

Arabic

Value	Count	Frequency (%)
ﺍ	131	11.8%
ﻟ	82	7.4%
ﺎ	61	5.5%
ﻮ	39	3.5%
ﺮ	39	3.5%
ﺔ	38	3.4%
ﻴ	37	3.3%
ﺭ	32	2.9%
ﻤ	31	2.8%
ﻠ	28	2.5%
Other values (91)	592	53.3%

Katakana

Value	Count	Frequency (%)
ン	37	7.4%
ル	34	6.8%
ス	24	4.8%
ラ	24	4.8%
リ	23	4.6%
ト	21	4.2%
プ	17	3.4%
ィ	17	3.4%
ア	16	3.2%
ド	16	3.2%
Other values (52)	269	54.0%

Hiragana

Value	Count	Frequency (%)
の	81	13.7%
る	47	7.9%
を	35	5.9%
に	33	5.6%
は	30	5.1%
な	29	4.9%
す	23	3.9%
で	21	3.5%
い	21	3.5%
れ	20	3.4%
Other values (36)	253	42.7%

Hangul

Value	Count	Frequency (%)
인	2	6.7%
라	2	6.7%
다	2	6.7%
만	1	3.3%
해	1	3.3%
도	1	3.3%
설	1	3.3%
레	1	3.3%
는	1	3.3%
이	1	3.3%
Other values (17)	17	56.7%

Unknown

Value	Count	Frequency (%)
	42	41.6%
	20	19.8%
	18	17.8%
	9	8.9%
	6	5.9%
	4	4.0%
	1	1.0%
	1	1.0%

Greek

Value	Count	Frequency (%)
β	7	28.0%
σ	5	20.0%
α	4	16.0%
Ε	2	8.0%
Β	2	8.0%
Ι	2	8.0%
Τ	2	8.0%
Δ	1	4.0%

Inherited

Value	Count	Frequency (%)
️	8	57.1%
̈	2	14.3%
̱	2	14.3%
⃣	1	7.1%
́	1	7.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	17228055	99.6%
Punctuation	43639	0.3%
None	30792	0.2%
Katakana	619	< 0.1%
Hiragana	593	< 0.1%
CJK	589	< 0.1%
Currency Symbols	444	< 0.1%
Geometric Shapes	369	< 0.1%
Letterlike Symbols	125	< 0.1%
PUA	101	< 0.1%
Other values (17)	219	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	2697010	15.7%
e	1573260	9.1%
t	1132630	6.6%
a	1088891	6.3%
n	993497	5.8%
i	992605	5.8%
o	967681	5.6%
r	886985	5.1%
s	873179	5.1%
l	516278	3.0%
Other values (87)	5506039	32.0%

None

Value	Count	Frequency (%)
	26172	85.0%
©	463	1.5%
®	429	1.4%
á	277	0.9%
é	226	0.7%
°	168	0.5%
¥	165	0.5%
£	146	0.5%
»	138	0.4%
ﺍ	131	0.4%
Other values (232)	2477	8.0%

Punctuation

Value	Count	Frequency (%)
’	15456	35.4%
“	9825	22.5%
”	9496	21.8%
—	5637	12.9%
–	2131	4.9%
‘	426	1.0%
…	253	0.6%
•	184	0.4%
	57	0.1%
	30	0.1%
Other values (12)	144	0.3%

Currency Symbols

Value	Count	Frequency (%)
€	423	95.3%
₹	21	4.7%

Geometric Shapes

Value	Count	Frequency (%)
△	311	84.3%
▪	25	6.8%
▲	19	5.1%
■	7	1.9%
◼	6	1.6%
○	1	0.3%

Letterlike Symbols

Value	Count	Frequency (%)
™	124	99.2%
℠	1	0.8%

Hiragana

Value	Count	Frequency (%)
の	81	13.7%
る	47	7.9%
を	35	5.9%
に	33	5.6%
は	30	5.1%
な	29	4.9%
す	23	3.9%
で	21	3.5%
い	21	3.5%
れ	20	3.4%
Other values (36)	253	42.7%

Katakana

Value	Count	Frequency (%)
・	67	10.8%
ー	54	8.7%
ン	37	6.0%
ル	34	5.5%
ス	24	3.9%
ラ	24	3.9%
リ	23	3.7%
ト	21	3.4%
プ	17	2.7%
ィ	17	2.7%
Other values (54)	301	48.6%

PUA

Value	Count	Frequency (%)
	42	41.6%
	20	19.8%
	18	17.8%
	9	8.9%
	6	5.9%
	4	4.0%
	1	1.0%
	1	1.0%

Alphabetic PF

Value	Count	Frequency (%)
ﬀ	12	92.3%
ﬁ	1	7.7%

Math Operators

Value	Count	Frequency (%)
≤	11	52.4%
−	5	23.8%
≥	3	14.3%
∆	1	4.8%
≈	1	4.8%

CJK

Value	Count	Frequency (%)
的	11	1.9%
業	10	1.7%
述	10	1.7%
記	10	1.7%
社	10	1.7%
会	9	1.5%
能	9	1.5%
関	9	1.5%
中	9	1.5%
来	8	1.4%
Other values (257)	494	83.9%

Enclosed Alphanum

Value	Count	Frequency (%)
Ⓡ	10	55.6%
①	3	16.7%
③	2	11.1%
②	2	11.1%
④	1	5.6%

Enclosed Alphanum Sup

Value	Count	Frequency (%)
🇵	9	15.5%
🇦	6	10.3%
🇭	5	8.6%
🇳	5	8.6%
🇬	4	6.9%
🇮	4	6.9%
🇯	4	6.9%
🇧	4	6.9%
🇿	3	5.2%
🇨	3	5.2%
Other values (7)	11	19.0%

VS

Value	Count	Frequency (%)
️	8	100.0%

Dingbats

Value	Count	Frequency (%)
✓	6	60.0%
➡	3	30.0%
❤	1	10.0%

Number Forms

Value	Count	Frequency (%)
Ⅰ	5	22.7%
Ⅱ	5	22.7%
Ⅲ	4	18.2%
Ⅳ	2	9.1%
Ⅵ	2	9.1%
Ⅶ	2	9.1%
Ⅴ	2	9.1%

Emoticons

Value	Count	Frequency (%)
😡	4	40.0%
🙌	4	40.0%
😃	1	10.0%
😁	1	10.0%

Arrows

Value	Count	Frequency (%)
→	3	100.0%

Latin Ext Additional

Value	Count	Frequency (%)
ỏ	2	28.6%
ạ	2	28.6%
ḵ	2	28.6%
ầ	1	14.3%

Hangul

Value	Count	Frequency (%)
인	2	6.7%
라	2	6.7%
다	2	6.7%
만	1	3.3%
해	1	3.3%
도	1	3.3%
설	1	3.3%
레	1	3.3%
는	1	3.3%
이	1	3.3%
Other values (17)	17	56.7%

Arabic

Value	Count	Frequency (%)
م	2	22.2%
،	2	22.2%
ر	1	11.1%
ح	1	11.1%
ا	1	11.1%
د	1	11.1%
ی	1	11.1%

Diacriticals

Value	Count	Frequency (%)
̈	2	40.0%
̱	2	40.0%
́	1	20.0%

Alchemical

Value	Count	Frequency (%)
🜃	1	100.0%

Modifier Letters

Value	Count	Frequency (%)
ˈ	1	50.0%
ː	1	50.0%

Math Alphanum

Value	Count	Frequency (%)
𝕏	1	100.0%

IPA Ext

Value	Count	Frequency (%)
ɔ	1	100.0%

article
Text

MISSING

Distinct	2972
Distinct (%)	100.0%
Missing	42584
Missing (%)	93.5%
Memory size	711.8 KiB

Length

Max length	204800
Median length	5066
Mean length	7612.7678
Min length	1

Characters and Unicode

Total characters	22625146
Distinct characters	894
Distinct categories	24 ?
Distinct scripts	13 ?
Distinct blocks	30 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2972 ?
Unique (%)	100.0%

Sample

1st row	pavyllon london, at four seasons hotel london at park lane one of the world’s best chefs has created a new culinary stage in london. yannick alléno, the superstar french chef who has nabbed an impressive 15 michelin stars, recently made his u.k. debut with the opening of pavyllon london. located in four seasons hotel london at park lane in fashionable mayfair, the new restaurant showcases alléno’s modern french cuisine with bespoke british influences. the fine dining destination offers guests a chance to experience alléno’s signature culinary trademarks — namely, modern french fare grounded in classical techniques, an innovative approach to sauce-making, and the belief that chefs have a responsibility to care for the good health of their guests — at his new refined-yet-relaxed culinary theater. "pavyllon london is an embodiment of my lifelong dream to redefine french dining," says alléno. "it combines my family's bistro-running legacy with my passion for culinary innovation. at pavyllon london, we look forward to welcoming our guests into the heart of the action, creating interactive, less formal dining, centered around countertop seating and simply great food.” chef yannick alléno chef yannick alléno pavyllon london is the latest restaurant from the prolific french-born yannick alléno, the superstar chef who’s been awarded 15 stars across 17 restaurants in paris, courchevel, monaco, dubai, and seoul. that puts alléno on a very short, elite list of michelin’s most-awarded chefs comprised of himself, joël robuchon, alain ducasse, gordon ramsay, pierre gagnaire, and martín berasategui. in fact, chef alléno’s trio of restaurants in pavillon ledoyen hold six stars (three at alléno paris, two at l’abysse and one at pavyllon), making it the most star-rated independent restaurant destination in the world. the lounge at pavyllon london at pavyllon london, which opened in july, chef yannick delivers his signature refined french fare with british influences like local and regional produce. the food, wine, and cocktail menus also feature specialty ingredients from time-honored purveyors like cheese from neal’s yard dairy and smoked salmon from h. forman & son in london, and sparkling wines from gusbourne vineyards in kent. alléno’s famed sauce-making skills and botanical extractions are on display throughout the menus. his groundbreaking methods utilize vacuum-cooking at precise temperatures, followed by cryo-concentration, to procure the purest flavor from produce and other ingredients. the result is vibrantly colored and flavored sauces that yield big flavor while reducing the amount of fat, sugar, and salt traditionally used. pavyllon london's menus showcase refined french cuisine with british influences pavyllon london’s cuisine pavyllon london serves breakfast, lunch, dinner, and high tea daily. crispy prawns with trout roe and miso beurre blanc a three-course lunch menu at pavyllon london might include crispy prawns set atop miso beurre blanc and trout roe, red mullet and clams with chermoula and chorizo butter, and a perfect baked peach baked in arlette crust with housemade red berry sorbet, topped with crunchy crystallized mint. the fine dining lunch set is designed to be eaten in just 55 minutes, meaning it’s possible to work in a decadent mid-day meal into an otherwise-ordinary work day. a refined version of surf and turf at dinner, guests can opt for à la carte options and kick off their meal with a duo of seabass carpaccio and beef tartare with puffed quinoa and crispy shallot to start, or a comté souffle with watercress coulis and eel butter. mains include a langoustine tart with caviar and beurre blanc, and veal cordon bleu with béchamel. desserts are inventive and intriguing, like roasted fig and curry ice cream with fig leaf cream, cardamom pastry crisp, and flambéed figs with marsala. highlights of the dinner tasting menus include warm potatoes glazed with an electric-green lovage mayonnaise, a reinvented prawn cocktail with fennel, dill, trout roe, and parmesan, lamb chops with shiso coulis and anchoïade, and beef filet with confit pears and flambéed cognac. high tea at pavyllon london at high tea, tiers of traditional fingers sandwiches are served alongside decadent chocolates and jewel-like miniature fruit tarts, meringues, and pastries covered in shiny mirror glaze. a sampling of small bites at bar antoine bar antoine before or after a meal at pavyllon, guests can stop off at bar antoine, helmed by head mixologist michele lombardi. the drinks menu ranges from classic cocktails to spirit-free libations, plus chef yannick’s signature creative gin & tonics made with seasonal fruit, vegetable, and herb extractions in a rainbow of vibrant colors. michele lombardi, head mixologist at bar antoine, crafts g&ts with botanical extractions bar antoine also offers shareable small plates, live jazz nights, and a lovely outdoor terrace for warmer months. a palette of soft blues and grays create a serene dining room design a sophisticated and serene palette of pearlescent whites, cool blues, and soft grays was curated by interior designer chahan minassian, the former european creative director of ralph lauren. the inspiration? “the chic conviviality of a parisian residence with the familiarity of a british club.” the tasteful lounge and main dining room feel sumptuous and serene, with gleaming marble, plush banquets, velvet sofas and cushions, contemporary art, and potted greenery like fiddle leaf figs. the chef's counter offers diners a front row seat to action in the kitchen the serene spaces are the ideal backdrop to showcase the culinary action of the open kitchen, which chef alléno views as a performance space. four seasons hotel london at park lane special holiday meals pavyllon london will be a destination for elevated festive holiday fare this winter, with special brunch, lunch, and dinner menus planned for christmas and new year’s. to learn more about pavyllon london in four seasons hotel london at park lane, or to book a reservation, visit pavyllon london.
2nd row	nice moved into provisional first place in the ligue 1 standings thanks to a second-half hicham boudaoui goal that secured a 1-0 win at lowly clermont foot on friday. the result lifted nice to first place with 22 points, two points ahead of as monaco, who play at lille on sunday. clermont are second-bottom with five points. boudaoui struck in the 74th minute after running unmarked into the box and tapping home a well-placed cross. nice had a chance to open the scoring just before halftime when alidu seidu fouled boudaoui in the area but gaetan labord's penalty went wide, leaving the first half scoreless. francesco farioli's nice have had an exceptional start to the season, maintaining an unbeaten record and securing seven clean sheets, the best in europe's top five leagues.
3rd row	the world’s frogs, salamanders, newts and other amphibians remain in serious trouble. a new global assessment has found that 41% of amphibian species that scientists have studied are threatened withextinction, meaning they are either vulnerable, endangered or critically endangered. that’s up from 39% reported in the last assessment, in 2004. “amphibians are the world's most threatened animals,” said duke university's junjie yao, a frog researcher who was not involved in the study. “their unique biology and permeable skin make them very sensitive to environmental changes." the study,published wednesdayin the journalnature, found that the loss of habitat from the expansion of farming and ranching is the single biggest threat to amphibians worldwide. but a growing percentage of amphibian species are now also pushed to the brink by novel diseases and climate change, the study found. read more:healthy biodiversity is the reason to fight climate change amphibians are especially vulnerable animals. they have distinct life stages that each often require separate habitats, so they can be disrupted by changes in either aquatic or land environments, said university of texas biologist michael ryan, who was not involved in the study. they are also at risk because of their delicate skin. most amphibians absorb oxygen to breathe through their skin, and so they do not have scales, feathers or fur to protect them. chemical pollution, bacteria and fungal infections impact them quickly, as do heightened swings in temperature and moisture levels due toclimate change. for example, frogs are usually nocturnal. if it’s too hot, they won’t come out even at night because they would lose too much water through their skin, said patricia burrowes, a study co-author and researcher at the national museum of natural sciences in madrid. but remaining in sheltered resting places limits frogs’ ability to eat and to breed. this summer was the hottest on record for the northern hemisphere, and 2023 is on track to be the 2nd hottest globally, after 2016. juan manuel guayasamin, a frog biologist at the university san francisco of quito, ecuador, said that advances in technology to trackanimalsand climate variations allowed the new study to use much more precise data than the 2004 assessment. “we have a much better understanding of some risks,” said guayasamin, who was not involved in the report. the study identified the greatest concentrations of threatened amphibian species in several biodiversity hotspots, including the caribbean islands, the tropical andes, madagascar and sri lanka. other locations with large numbers of threatened amphibians include brazil’s atlantic forest, southern china and the southeastern united states.
4th row	iron-rich sediment colors the red-orange waters of the betsiboka river delta in madagascar in this image taken by an astronaut on the international space station on sept. 30, 2023. the sediment can clog waterways in the delta's estuarial environment, but it can also form new islands that become colonized by mangroves.despite its rusty color, this artery of water is important for biodiversity. within the betsiboka river delta, the estuary supplies food, such as seagrasses, to the endangered green turtle and vulnerable dugong, or sea cow.provided bynasacitation: image: rusty red waters in madagascar (2023, october 31) retrieved 3 november 2023 from https://phys.org/news/2023-10-image-rusty-red-madagascar.htmlthis document is subject to copyright. apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. the content is provided for information purposes only. despite its rusty color, this artery of water is important for biodiversity. within the betsiboka river delta, the estuary supplies food, such as seagrasses, to the endangered green turtle and vulnerable dugong, or sea cow.provided bynasacitation: image: rusty red waters in madagascar (2023, october 31) retrieved 3 november 2023 from https://phys.org/news/2023-10-image-rusty-red-madagascar.htmlthis document is subject to copyright. apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. the content is provided for information purposes only. despite its rusty color, this artery of water is important for biodiversity. within the betsiboka river delta, the estuary supplies food, such as seagrasses, to the endangered green turtle and vulnerable dugong, or sea cow.provided bynasacitation: image: rusty red waters in madagascar (2023, october 31) retrieved 3 november 2023 from https://phys.org/news/2023-10-image-rusty-red-madagascar.htmlthis document is subject to copyright. apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. the content is provided for information purposes only. provided bynasa
5th row	everything ends. no, i’m not having an existential crisis; i’m looking atmax‘s lineup in november! while it’s true that the streamer will be adding lots of new shows and movies, it will also be saying goodbye to quite a few films and series in november. probably the most significant things leaving max this month are the amc+ showsadiscovery of witches,dark winds,killing eve,interview with the vampire,ride with norman reedus,fear the walking dead, andgangs of london. this isn’t surprising as they were on a three-month loan from the rival streamer. other movies leaving the service include theunderrated horror sequela nightmare on elm street 4, the steamy 1994 thrillerdisclosure, and the classic suburban nightmare filmpoltergeist. check out the list below and try to watch some of these gems before they leave max for good (or at least a long while). editor’s note: this list may not be comprehensive and is subject to change by the network/streamer. november 1 dark winds (amc+) a discovery of witches, season 1-3 (amc+) fear the walking dead, seasons 1-7 (amc+) gangs of london, seasons 1-2 (amc+) interview with the vampire (amc+) killing eve, seasons 1-4 (amc+) ride with norman reedus, seasons 1-5 (amc+) november 2 300: rise of an empire (2014) november 5 hard knocks: in season: the arizona cardinals (hbo original) november 6 the host (2013) november 7 noblesse we are not done yet (2018) (hbo original) november 12 banksy does new york (hbo original) november 14 tsukimichi – moonlit fantasy november 15 2022 rock & roll hall of fame induction ceremony (hbo original) november 25 the howard stern interview: bruce springsteen (hbo original) november 30 10,000 b.c. (2008) absolute power (1997) adam ruins everything, season 2-3 angels in the outfield (1951) the apparition (2012) the asphalt jungle (1950) badlands (1973) black sheep (1996) blade (1998) blade ii (2002) blade: trinity (2004) breach (2007) burn after reading (2008) the carbonaro effect, seasons 2-5 cats (2019) cleopatra (1963) comedy knockout critters (1986) critters 3 (1991) the curse of frankenstein (1957) dark shadows (2012) de blanco la patuda (aka white is for virgins) (2020) the descent (2006) the descent: part 2 (2009) diggers (2007) disclosure (1994) doc hollywood (1991) doctor sleep (2019) dracula a.d. 1972 (1972) dracula has risen from the grave (1969) draft day (2014) the drop (2014) first reformed (2018) the fly (1986) the forbidden kingdom (2008) freddy vs. jason (2003) freddy’s dead: the final nightmare (1991) friday the 13th (2009) gemini (2018) gone girl (2014) green room (2016) the haunting (1963) horror of dracula (1958) the hurt locker (2009) i origins (2014) if beale street could talk (2018) insidious (2010) into the forest (2016) journey 2: the mysterious island (2012) krisha (2016) lakeview terrace (2008) limitless (2011) los dias de la ballena (aka the days of the whale) (2019) lucas (1986) lucky you (2007) madagascar: escape 2 africa (2008) the mask (1994) the maze runner (2014) the middle moonfall (2022) the mummy (1959) narc (2003) the neverending story ii: the next chapter (1991) a nightmare on elm street (1984) a nightmare on elm street 2: freddy’s revenge (1985) a nightmare on elm street 3: dream warriors (1987) a nightmare on elm street 4: the dream master (1988) a nightmare on elm street 5: the dream child (1989) the outsiders (1983) paid off with michael torpey poltergeist (1982) pretty in pink (1986) private benjamin (1980) the purge: anarchy (2014) rachel dratch’s late night snack reindeer games (2000) the rookie (1990) safe haven (2013) the sea of trees (2016) see no evil, hear no evil (1989) the show (2017) signs (2002) some kind of wonderful (1987) soylent green (1973) stephen king’s cat’s eye (1985) talk show the game show team america: world police (2004) temptation: confessions of a marriage counselor (2013) urban cowboy (1980) v for vendetta (2005) wild rose (2019) young frankenstein (1974)

Value	Count	Frequency (%)
the	200965	5.7%
of	100391	2.9%
and	96506	2.8%
to	94307	2.7%
in	76020	2.2%
a	63249	1.8%
for	36162	1.0%
is	33047	0.9%
on	30628	0.9%
that	30061	0.9%
Other values (110059)	2739299	78.3%

Most occurring characters

Value	Count	Frequency (%)
	3517689	15.5%
e	2147616	9.5%
t	1595255	7.1%
a	1576392	7.0%
i	1394450	6.2%
o	1336444	5.9%
n	1335995	5.9%
s	1204029	5.3%
r	1197612	5.3%
h	747245	3.3%
Other values (884)	6572419	29.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	17970759	79.4%
Space Separator	3529247	15.6%
Other Punctuation	508905	2.2%
Decimal Number	331093	1.5%
Control	90977	0.4%
Dash Punctuation	74552	0.3%
Final Punctuation	35263	0.2%
Open Punctuation	24050	0.1%
Close Punctuation	24029	0.1%
Initial Punctuation	16657	0.1%
Other values (14)	19614	0.1%

Most frequent character per category

Other Letter

Value	Count	Frequency (%)
ﺍ	131	4.3%
ﻟ	82	2.7%
の	81	2.7%
ﺎ	61	2.0%
る	47	1.6%
ﺮ	39	1.3%
ﻮ	39	1.3%
ﺔ	38	1.3%
ン	37	1.2%
ﻴ	37	1.2%
Other values (515)	2420	80.3%

Lowercase Letter

Value	Count	Frequency (%)
e	2147616	12.0%
t	1595255	8.9%
a	1576392	8.8%
i	1394450	7.8%
o	1336444	7.4%
n	1335995	7.4%
s	1204029	6.7%
r	1197612	6.7%
h	747245	4.2%
l	722331	4.0%
Other values (87)	4713390	26.2%

Other Symbol

Value	Count	Frequency (%)
©	1066	59.8%
°	293	16.4%
®	129	7.2%
™	43	2.4%
¦	23	1.3%
▲	19	1.1%
℃	12	0.7%
🇵	10	0.6%
👏	9	0.5%
├	8	0.4%
Other values (79)	172	9.6%

Other Punctuation

Value	Count	Frequency (%)
,	215824	42.4%
.	191226	37.6%
"	28142	5.5%
'	24993	4.9%
:	15580	3.1%
/	12757	2.5%
%	8334	1.6%
?	3561	0.7%
;	3297	0.6%
&	2078	0.4%
Other values (23)	3113	0.6%

Uppercase Letter

Value	Count	Frequency (%)
T	56	9.1%
S	53	8.6%
P	44	7.1%
N	41	6.6%
G	40	6.5%
D	37	6.0%
A	35	5.7%
O	30	4.9%
K	27	4.4%
M	26	4.2%
Other values (19)	229	37.1%

Decimal Number

Value	Count	Frequency (%)
0	73598	22.2%
2	72189	21.8%
1	46305	14.0%
3	37305	11.3%
4	21003	6.3%
5	20379	6.2%
9	16819	5.1%
6	14968	4.5%
7	14488	4.4%
8	14023	4.2%
Other values (7)	16	< 0.1%

Math Symbol

Value	Count	Frequency (%)
+	1353	72.1%
\|	273	14.6%
~	105	5.6%
=	41	2.2%
＋	38	2.0%
>	23	1.2%
≤	11	0.6%
<	9	0.5%
◼	6	0.3%
±	6	0.3%
Other values (6)	11	0.6%

Nonspacing Mark

Value	Count	Frequency (%)
้	14	21.2%
️	11	16.7%
่	9	13.6%
́	7	10.6%
ั	6	9.1%
ี	4	6.1%
်	2	3.0%
ื	2	3.0%
ึ	2	3.0%
็	2	3.0%
Other values (6)	7	10.6%

Open Punctuation

Value	Count	Frequency (%)
(	23126	96.2%
[	889	3.7%
（	14	0.1%
‚	9	< 0.1%
„	4	< 0.1%
〔	3	< 0.1%
「	3	< 0.1%
【	2	< 0.1%

Close Punctuation

Value	Count	Frequency (%)
)	23110	96.2%
]	887	3.7%
）	14	0.1%
⁾	10	< 0.1%
〕	3	< 0.1%
」	3	< 0.1%
】	2	< 0.1%

Currency Symbol

Value	Count	Frequency (%)
$	10186	94.4%
€	411	3.8%
£	116	1.1%
₹	51	0.5%
¥	18	0.2%
₦	2	< 0.1%
＄	1	< 0.1%

Modifier Letter

Value	Count	Frequency (%)
ー	54	73.0%
ʻ	14	18.9%
ˇ	2	2.7%
ʺ	1	1.4%
々	1	1.4%
ˈ	1	1.4%
ː	1	1.4%

Modifier Symbol

Value	Count	Frequency (%)
´	8	28.6%
^	6	21.4%
🏻	5	17.9%
🏽	4	14.3%
🏼	2	7.1%
`	2	7.1%
˘	1	3.6%

Control

Value	Count	Frequency (%)
	82936	91.2%
	8036	8.8%
	2	< 0.1%
	1	< 0.1%
	1	< 0.1%
	1	< 0.1%

Space Separator

Value	Count	Frequency (%)
	3517689	99.7%
	11512	0.3%
	24	< 0.1%
	12	< 0.1%
	10	< 0.1%

Dash Punctuation

Value	Count	Frequency (%)
-	58006	77.8%
–	10229	13.7%
—	6300	8.5%
‐	16	< 0.1%
‑	1	< 0.1%

Format

Value	Count	Frequency (%)
	66	58.9%
	24	21.4%
	8	7.1%
‎	8	7.1%
⁣	6	5.4%

Other Number

Value	Count	Frequency (%)
²	46	65.7%
₂	10	14.3%
¹	9	12.9%
½	5	7.1%

Final Punctuation

Value	Count	Frequency (%)
’	22107	62.7%
”	13041	37.0%
»	115	0.3%

Initial Punctuation

Value	Count	Frequency (%)
“	15699	94.2%
‘	956	5.7%
«	2	< 0.1%

Spacing Mark

Value	Count	Frequency (%)
ြ	1	50.0%
း	1	50.0%

Connector Punctuation

Value	Count	Frequency (%)
_	1182	100.0%

Private Use

Value	Count	Frequency (%)
	4	100.0%

Enclosing Mark

Value	Count	Frequency (%)
⃣	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	17971349	79.4%
Common	4650696	20.6%
Arabic	1110	< 0.1%
Han	595	< 0.1%
Hiragana	593	< 0.1%
Katakana	498	< 0.1%
Thai	246	< 0.1%
Inherited	21	< 0.1%
Greek	13	< 0.1%
Myanmar	12	< 0.1%
Other values (3)	13	< 0.1%

Most frequent character per script

Han

Value	Count	Frequency (%)
的	11	1.8%
記	10	1.7%
業	10	1.7%
述	10	1.7%
社	10	1.7%
会	9	1.5%
関	9	1.5%
能	9	1.5%
中	9	1.5%
来	8	1.3%
Other values (263)	500	84.0%

Common

Value	Count	Frequency (%)
	3517689	75.6%
,	215824	4.6%
.	191226	4.1%
	82936	1.8%
0	73598	1.6%
2	72189	1.6%
-	58006	1.2%
1	46305	1.0%
3	37305	0.8%
"	28142	0.6%
Other values (219)	327476	7.0%

Latin

Value	Count	Frequency (%)
e	2147616	12.0%
t	1595255	8.9%
a	1576392	8.8%
i	1394450	7.8%
o	1336444	7.4%
n	1335995	7.4%
s	1204029	6.7%
r	1197612	6.7%
h	747245	4.2%
l	722331	4.0%
Other values (102)	4713980	26.2%

Arabic

Value	Count	Frequency (%)
ﺍ	131	11.8%
ﻟ	82	7.4%
ﺎ	61	5.5%
ﺮ	39	3.5%
ﻮ	39	3.5%
ﺔ	38	3.4%
ﻴ	37	3.3%
ﺭ	32	2.9%
ﻤ	31	2.8%
ﻠ	28	2.5%
Other values (91)	592	53.3%

Katakana

Value	Count	Frequency (%)
ン	37	7.4%
ル	34	6.8%
ス	24	4.8%
ラ	24	4.8%
リ	23	4.6%
ト	21	4.2%
プ	17	3.4%
ィ	17	3.4%
ア	16	3.2%
ド	16	3.2%
Other values (52)	269	54.0%

Hiragana

Value	Count	Frequency (%)
の	81	13.7%
る	47	7.9%
を	35	5.9%
に	33	5.6%
は	30	5.1%
な	29	4.9%
す	23	3.9%
で	21	3.5%
い	21	3.5%
れ	20	3.4%
Other values (36)	253	42.7%

Thai

Value	Count	Frequency (%)
า	22	8.9%
ร	20	8.1%
้	14	5.7%
อ	12	4.9%
ง	11	4.5%
ก	10	4.1%
น	10	4.1%
่	9	3.7%
เ	9	3.7%
ด	8	3.3%
Other values (35)	121	49.2%

Myanmar

Value	Count	Frequency (%)
်	2	16.7%
မ	2	16.7%
က	2	16.7%
ု	1	8.3%
အ	1	8.3%
ြ	1	8.3%
း	1	8.3%
ဖ	1	8.3%
ှ	1	8.3%

Greek

Value	Count	Frequency (%)
β	6	46.2%
τ	2	15.4%
ι	2	15.4%
ε	2	15.4%
α	1	7.7%

Inherited

Value	Count	Frequency (%)
️	11	52.4%
́	7	33.3%
̱	2	9.5%
⃣	1	4.8%

Hangul

Value	Count	Frequency (%)
다	2	33.3%
라	2	33.3%
박	1	16.7%
산	1	16.7%

Cyrillic

Value	Count	Frequency (%)
ф	1	33.3%
ї	1	33.3%
а	1	33.3%

Unknown

Value	Count	Frequency (%)
	4	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	22535173	99.6%
Punctuation	68983	0.3%
None	18130	0.1%
Katakana	617	< 0.1%
CJK	594	< 0.1%
Hiragana	593	< 0.1%
Currency Symbols	464	< 0.1%
Thai	246	< 0.1%
Enclosed Alphanum Sup	68	< 0.1%
Letterlike Symbols	56	< 0.1%
Other values (20)	222	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	3517689	15.6%
e	2147616	9.5%
t	1595255	7.1%
a	1576392	7.0%
i	1394450	6.2%
o	1336444	5.9%
n	1335995	5.9%
s	1204029	5.3%
r	1197612	5.3%
h	747245	3.3%
Other values (85)	6482446	28.8%

Punctuation

Value	Count	Frequency (%)
’	22107	32.0%
“	15699	22.8%
”	13041	18.9%
–	10229	14.8%
—	6300	9.1%
‘	956	1.4%
…	378	0.5%
•	150	0.2%
	24	< 0.1%
	24	< 0.1%
Other values (12)	75	0.1%

None

Value	Count	Frequency (%)
	11512	63.5%
©	1066	5.9%
é	899	5.0%
ñ	466	2.6%
á	350	1.9%
°	293	1.6%
í	189	1.0%
ó	164	0.9%
ü	141	0.8%
ﺍ	131	0.7%
Other values (237)	2919	16.1%

Currency Symbols

Value	Count	Frequency (%)
€	411	88.6%
₹	51	11.0%
₦	2	0.4%

Hiragana

Value	Count	Frequency (%)
の	81	13.7%
る	47	7.9%
を	35	5.9%
に	33	5.6%
は	30	5.1%
な	29	4.9%
す	23	3.9%
で	21	3.5%
い	21	3.5%
れ	20	3.4%
Other values (36)	253	42.7%

Katakana

Value	Count	Frequency (%)
・	65	10.5%
ー	54	8.8%
ン	37	6.0%
ル	34	5.5%
ス	24	3.9%
ラ	24	3.9%
リ	23	3.7%
ト	21	3.4%
プ	17	2.8%
ィ	17	2.8%
Other values (54)	301	48.8%

Letterlike Symbols

Value	Count	Frequency (%)
™	43	76.8%
℃	12	21.4%
℠	1	1.8%

Thai

Value	Count	Frequency (%)
า	22	8.9%
ร	20	8.1%
้	14	5.7%
อ	12	4.9%
ง	11	4.5%
ก	10	4.1%
น	10	4.1%
่	9	3.7%
เ	9	3.7%
ด	8	3.3%
Other values (35)	121	49.2%

Geometric Shapes

Value	Count	Frequency (%)
▲	19	42.2%
▪	7	15.6%
■	7	15.6%
◼	6	13.3%
◉	2	4.4%
●	2	4.4%
◦	2	4.4%

Alphabetic PF

Value	Count	Frequency (%)
ﬁ	15	100.0%

Modifier Letters

Value	Count	Frequency (%)
ʻ	14	70.0%
ˇ	2	10.0%
˘	1	5.0%
ʺ	1	5.0%
ˈ	1	5.0%
ː	1	5.0%

Math Operators

Value	Count	Frequency (%)
≤	11	73.3%
−	3	20.0%
≥	1	6.7%

CJK

Value	Count	Frequency (%)
的	11	1.9%
記	10	1.7%
業	10	1.7%
述	10	1.7%
社	10	1.7%
会	9	1.5%
関	9	1.5%
能	9	1.5%
中	9	1.5%
来	8	1.3%
Other values (262)	499	84.0%

VS

Value	Count	Frequency (%)
️	11	100.0%

Enclosed Alphanum Sup

Value	Count	Frequency (%)
🇵	10	14.7%
🇳	7	10.3%
🇦	6	8.8%
🇭	5	7.4%
🇿	5	7.4%
🇯	4	5.9%
🇧	4	5.9%
🇬	4	5.9%
🇨	4	5.9%
🇮	4	5.9%
Other values (9)	15	22.1%

Box Drawing

Value	Count	Frequency (%)
├	8	53.3%
┬	4	26.7%
┤	2	13.3%
│	1	6.7%

Diacriticals

Value	Count	Frequency (%)
́	7	77.8%
̱	2	22.2%

Emoticons

Value	Count	Frequency (%)
🙌	6	26.1%
😡	4	17.4%
🙏	4	17.4%
😁	2	8.7%
😍	2	8.7%
😅	1	4.3%
😊	1	4.3%
🙂	1	4.3%
😘	1	4.3%
😃	1	4.3%

Math Alphanum

Value	Count	Frequency (%)
𝐚	4	30.8%
𝐧	3	23.1%
𝐲	2	15.4%
𝐀	1	7.7%
𝐝	1	7.7%
𝐏	1	7.7%
𝕏	1	7.7%

PUA

Value	Count	Frequency (%)
	4	100.0%

Misc Symbols

Value	Count	Frequency (%)
♥	3	100.0%

Dingbats

Value	Count	Frequency (%)
➡	3	42.9%
✿	2	28.6%
✊	1	14.3%
❤	1	14.3%

Myanmar

Value	Count	Frequency (%)
်	2	16.7%
မ	2	16.7%
က	2	16.7%
ု	1	8.3%
အ	1	8.3%
ြ	1	8.3%
း	1	8.3%
ဖ	1	8.3%
ှ	1	8.3%

Latin Ext Additional

Value	Count	Frequency (%)
ạ	2	28.6%
ỏ	2	28.6%
ḵ	2	28.6%
ầ	1	14.3%

Arrows

Value	Count	Frequency (%)
→	2	66.7%
↓	1	33.3%

Arabic

Value	Count	Frequency (%)
،	2	22.2%
م	2	22.2%
ا	1	11.1%
د	1	11.1%
ی	1	11.1%
ر	1	11.1%
ح	1	11.1%

Hangul

Value	Count	Frequency (%)
다	2	33.3%
라	2	33.3%
박	1	16.7%
산	1	16.7%

Cyrillic

Value	Count	Frequency (%)
ф	1	33.3%
ї	1	33.3%
а	1	33.3%

IPA Ext

Value	Count	Frequency (%)
ɔ	1	100.0%

Alchemical

Value	Count	Frequency (%)
🜃	1	100.0%

title_sentiment
Categorical

MISSING

Distinct	3
Distinct (%)	0.1%
Missing	42584
Missing (%)	93.5%
Memory size	711.8 KiB

Neutral	2050
Negative	640
Positive	282

Length

Max length	8
Median length	7
Mean length	7.3102288
Min length	7

Characters and Unicode

Total characters	21726
Distinct characters	13
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Neutral
2nd row	Positive
3rd row	Negative
4th row	Neutral
5th row	Neutral

Common Values

Value	Count	Frequency (%)
Neutral	2050	4.5%
Negative	640	1.4%
Positive	282	0.6%
(Missing)	42584	93.5%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
neutral	2050	69.0%
negative	640	21.5%
positive	282	9.5%

Most occurring characters

Value	Count	Frequency (%)
e	3612	16.6%
t	2972	13.7%
N	2690	12.4%
a	2690	12.4%
u	2050	9.4%
r	2050	9.4%
l	2050	9.4%
i	1204	5.5%
v	922	4.2%
g	640	2.9%
Other values (3)	846	3.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	18754	86.3%
Uppercase Letter	2972	13.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3612	19.3%
t	2972	15.8%
a	2690	14.3%
u	2050	10.9%
r	2050	10.9%
l	2050	10.9%
i	1204	6.4%
v	922	4.9%
g	640	3.4%
o	282	1.5%

Uppercase Letter

Value	Count	Frequency (%)
N	2690	90.5%
P	282	9.5%

Most occurring scripts

Value	Count	Frequency (%)
Latin	21726	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3612	16.6%
t	2972	13.7%
N	2690	12.4%
a	2690	12.4%
u	2050	9.4%
r	2050	9.4%
l	2050	9.4%
i	1204	5.5%
v	922	4.2%
g	640	2.9%
Other values (3)	846	3.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	21726	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	3612	16.6%
t	2972	13.7%
N	2690	12.4%
a	2690	12.4%
u	2050	9.4%
r	2050	9.4%
l	2050	9.4%
i	1204	5.5%
v	922	4.2%
g	640	2.9%
Other values (3)	846	3.9%

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

First rows
Last rows

	article_id	source_id	source_name	author	title	description	url	url_to_image	published_at	content	category	full_content	article	title_sentiment
0	89541	NaN	International Business Times	Paavan MATHEMA	UN Chief Urges World To 'Stop The Madness' Of Climate Change	UN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastating impact of the phenomenon.	https://www.ibtimes.com/un-chief-urges-world-stop-madness-climate-change-3716939	https://d.ibtimes.com/en/full/4496078/nepals-glaciers-melted-65-percent-faster-last-decade-previous-one-said-guterres-who.jpg	2023-10-30 10:12:35.000000	UN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastatin… [+2444 chars]	Nepal	UN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastating impact of the phenomenon. "The rooftops of the world are caving in," Guterres said on a visit to the Everest region in mountainous Nepal, adding that the country had lost nearly a third of its ice in just over three decades. "Glaciers are icy reservoirs -- the ones here in the Himalayas supply fresh water to well over a billion people," he said. "When they shrink, so do river flows." Nepal's glaciers melted 65 percent faster in the last decade than in the previous one, said Guterres, who is on a four-day visit to Nepal. Glaciers in the wider Himalayan and Hindu Kush ranges are a crucial water source for around 240 million people in the mountainous regions, as well as for another 1.65 billion people in the South Asian and Southeast Asian river valleys below. The glaciers feed 10 of the world's most important river systems, including the Ganges, Indus, Yellow, Mekong and Irrawaddy, and directly or indirectly supply billions of people with food, energy, clean air and income. Scientists say they are melting faster than ever before due to climate change, exposing communities to unpredictable and costly disasters. "I am here today to cry out from the rooftop of the world: stop the madness", Guterres said, speaking from Syangboche village, with the icy peak of the world's highest mountain Everest towering behind him. "The glaciers are retreating, but we cannot. We must end the fossil fuel age," he said. The world has warmed an average of nearly 1.2 degrees Celsius since the mid-1800s, unleashing a cascade of extreme weather, including more intense heatwaves, more severe droughts and storms made more ferocious by rising seas. Hardest hit are the most vulnerable people and the world's poorest countries, which have done little to contribute to the fossil fuel emissions that drive up temperatures. "We must act now to protect people on the frontline, and to limit global temperature rise to 1.5 degrees, to avert the worst of climate chaos," Guterres said. "The world can't wait." In the first phase of climate change's effects, melting glaciers can trigger destructive floods. "Melting glaciers mean swollen lakes and rivers flooding, sweeping away entire communities", he added. But all too soon, glaciers will dry up if change is not made, he warned. "In the future, major Himalayan rivers like the Indus, the Ganges and Brahmaputra could have massively reduced flows, he said. "That spells catastrophe".	NaN	NaN
1	89542	NaN	Prtimes.jp	NaN	RANDEBOOよりワンランク上の大人っぽさが漂うニットとベストが新登場。	[株式会社Ainer]\nRANDEBOO（ランデブー）では2023年7月18日(火)より公式WEBストアにて2023 Autumn Winter コレクションの発売を開始。\nシーズンコレクションとして、"Nepal Handmade ram vest"と”Nepal Handmade ram kn...	https://prtimes.jp/main/html/rd/p/000000147.000032220.html	https://prtimes.jp/i/32220/147/ogp/d32220-147-21916971e381da87f7a9-0.jpg	2023-10-06 04:40:02.000000	RANDEBOO2023718()WEB2023 Autumn Winter \n"Nepal Handmade ram vest"Nepal Handmade ram knit"2023106()12:00WEB\n106() 12:00\nhttps://x.gd/226uY\nNepal Handmade ram vest\n¥17,600()\nGray/Ivory\n[]\n1\n[D… [+583 chars]	Nepal	NaN	NaN	NaN
2	89543	NaN	VOA News	webdesk@voanews.com (Agence France-Presse)	UN Chief Urges World to 'Stop the Madness' of Climate Change	UN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witness the devastating impact of the phenomenon.\n\n\n"The rooftops of the world are…	https://www.voanews.com/a/un-chief-urges-world-to-stop-the-madness-of-climate-change-/7332605.html	https://gdb.voanews.com/01000000-0a00-0242-60fd-08dbd93618b1_w1200_r1.jpg	2023-10-30 10:53:30.000000	Kathmandu, Nepal UN Secretary-General Antonio Guterres urged the world Monday to "stop the madness" of climate change as he visited Himalayan regions struggling from rapidly melting glaciers to witn… [+2468 chars]	Nepal	NaN	NaN	NaN
3	89545	NaN	The Indian Express	Editorial	Sikkim warning: Hydroelectricity push must be accompanied by safety measures	Ecologists caution against the adverse effects of dam construction — it increases the volatility of rocks in the Himalayan region. Wednesday's disaster is a warning to take such caveats seriously	https://indianexpress.com/article/opinion/editorials/sikkim-warning-hydroelectricity-push-must-be-accompanied-by-safety-measures-8970365/	https://images.indianexpress.com/2023/10/edit-4.jpg	2023-10-06 01:20:24.000000	At least 14 persons lost their lives and more than 100 others, including 23 army personnel, are reportedly missing in Sikkim after the Teesta river went into spate on Wednesday. The flash floods seem… [+1826 chars]	Nepal	At least 14 persons lost their lives and more than 100 others, including 23 army personnel, are reportedly missing in Sikkim after the Teesta river went into spate on Wednesday. The flash floods seem to have been triggered by a combination of factors. A cloud burst ripped apart the South Shonak Lake — a glacial body in the state’s northwest. According to Sikkim’s Disaster Management Authority (SDMA), the Teesta has inundated at least four districts in the state. The calamity was aggravated by the release of water from the Chungthang dam — initial reports suggest that the breach was caused by water rushing from the mountains. Disaster management authorities are also investigating the possibility of the event being triggered by an earthquake in Nepal on Tuesday. The probe is likely to throw more light on the immediate causes of the flash flood but one thing has long been clear — states in the Himalayan region must respect the fragile ecology of the mountains and put in adequate safeguards to mitigate the damage caused by increasingly frequent extreme rainfall events. For years, studies have red-flagged the South Shonak Lake’s expansion due to glacial melting and warned that the water body is susceptible to breaches. In 2021, for instance, a study by scientists from IIT Roorkee, Indian Institute of ScienceBangalore, University of Dayton, USA, University of Graz, Austria, and the Universities of Zurich and Geneva in Switzerland recommended regular monitoring of the lake’s growth and continuous assessment of the region’s slope stability. The National Disaster Management Authority guidelines also say that risk reduction has to begin with mapping such water bodies, taking structural measures to prevent their breach and establishing mechanisms that can alert people about glacial lake outbursts. The IMD has collaborated with the US National Weather Service to warn people about six to 24 hours before a flash flood. But the system doesn’t seem to have come to terms with the Himalayan region’s idiosyncrasies. The Northeast has a key place in the hydel power push of successive governments at the Centre. The Chungthang Dam is a part of the 1,200 MW Teesta Stage 3 Hydroelectric Project. The government claims that such projects are climate-friendly because of their low emissions intensity. Hydroelectric power is also a major source of revenue for Sikkim. Ecologists, however, caution against the adverse effects of dam construction — it increases the volatility of rocks in the Himalayan region. Wednesday’s disaster is a warning to take such caveats seriously and install robust safety mechanisms.	NaN	NaN
4	89547	NaN	The Times of Israel	Jacob Magid	200 foreigners, dual nationals cut down in Hamas assault. Here’s where they were from	France lost 35 citizens, Thailand 33, US 31, Ukraine 21, Russia 19, UK 12, Nepal 10, Germany 10, Argentina 9, Canada 6, Romania 5, Portuguese 4, China 4, Philippines 4, Austria 4\nThe post 200 foreigners, dual nationals cut down in Hamas assault. Here’s where …	https://www.timesofisrael.com/200-foreigners-dual-nationals-cut-down-in-hamas-assault-heres-where-they-were-from/	https://static.timesofisrael.com/www/uploads/2023/10/231011160737-emily-hand-beeri-victim.jpg	2023-10-27 01:08:34.000000	Scores of foreign citizens were killed, taken hostage or listed as missing after the Hamas terror group launched a major assault on Israel on October 7.\nMore than 1,400, mainly civilians, have been … [+4658 chars]	Nepal	NaN	NaN	NaN
5	89550	NaN	BBC News	https://www.facebook.com/bbcworldservice/	中印交惡令尼泊爾機場難以發展國際航線	尼泊爾花費數百萬美元建造一座新機場，希望藉此推動旅遊業——但是機場建好了，卻沒有人來。	https://www.bbc.com/zhongwen/trad/world-67015581	https://ichef.bbci.co.uk/news/1024/branded_zhongwen/1377C/production/_131304797_gettyimages-1240701029.jpg	2023-10-05 07:30:45.000000	Getty Images\n20225\n·Bishnu Sharma\nLumbini\nLumbini Development Trust2022100760061005Gautam Buddha International Airport\nBhairahawa Airport250155\nBBC\nBBC/Anbarasan Ethirajan\n12\nGetty Images\n2… [+196 chars]	Nepal	NaN	NaN	NaN
6	89551	al-jazeera-english	Al Jazeera English	Kaushik Raj	Pro-Israel rallies allowed in India but Palestine solidarity sees crackdown	India, the first non-Arab country to recognise the PLO, is now seen closer to Israel and its biggest benefactor, the US.	https://www.aljazeera.com/news/2023/10/25/pro-israel-rallies-allowed-in-india-but-palestine-solidarity-sees-crackdown	https://www.aljazeera.com/wp-content/uploads/2023/10/2023-10-20T131413Z_1502521591_RC2AW3AED0YK_RTRMADP_3_ISRAEL-PALESTINIANS-INDIA-PROTEST-1698223702.jpg?resize=1920%2C1440	2023-10-25 09:58:17.000000	New Delhi, India Israels relentless bombing of the besieged Gaza Strip and killing of nearly 6,000 people a third of them children in two weeks has outraged people across the world, triggering mass p… [+9013 chars]	Nepal	India, the first non-Arab country to recognise the PLO in the 1970s, is now seen closer to Israel and its biggest benefactor, the United States. New Delhi, India– Israel’srelentless bombingof the besieged Gaza Strip and killing of nearly 6,000 people – a third of them children – in two weeks has outraged people across the world, triggering mass protests and a call for an immediate ceasefire. However, in India – the first non-Arab country to recognise the Palestine Liberation Organization (PLO), but now seen closer to Israel and its biggest benefactor, the United States – some pro-Palestine protesters reported being targeted by the government. Less than a week after the Gaza assault began, police in Hamirpur district of India’s most populous Uttar Pradesh state were looking for Muslim scholars Atif Chaudhary and Suhail Ansari. Their alleged crime: putting a WhatsApp display photo that said: “I stand with Palestine.” The two men were charged with promoting enmity between social groups. Ansari is under arrest, while Chaudhary is on the run, according to the police. In the same state, governed by the Hindu nationalist Bharatiya Janata Party (BJP), four students of the Aligarh Muslim University were booked by the police after they took out a pro-Palestine march on the campus a day after the Gaza assault began on October 7. However, when the Hindu far-right group Bajrang Dal took out a pro-Israel march in the same Aligarh city, raising slogans such as “Down with Palestine, Down with Hamas”, no action was taken against them by the authorities. In the national capital, New Delhi, there have been several examples of people being detained during rallies organised by student groups, activists and citizens for solidarity with the Palestinians since October 7. In the western state of Maharashtra, also governed by the BJP in alliance with a regional party, two protesters, Ruchir Lad and Supreeth Ravish, were arrested on October 13 for holding a march against the war on Gaza and charged with unlawful assembly. Pooja Chinchole, member of the Revolutionary Workers Party of India and one of the organisers of the protest held in state capital Mumbai, told Al Jazeera the police “created many hurdles before us when they got to know that we are organising a pro-Palestine protest”. “They detained one of the organisers a day before the protest and three organisers on the morning of the protest. When we still gathered to protest, they snatched our microphone, placards, and after a while, started using force on some of us,” she said. The crackdown, however, was not limited to the BJP-ruled states only. In the southern Karnataka state, governed by the main opposition Congress party, police charged 10 activists with creating a public nuisance after they organised a silent march in support of the Palestinians on October 16 in Bengaluru, the capital of the state. The Karnataka police also arrested a 58-year-old Muslim man for allegedly posting a video in support of Hamas on WhatsApp. Police also briefly detained Alam Nawaz, a Muslim government employee, for updating his WhatsApp status with a Palestinian flag and “Long Live Palestine” message. “People started seeing me with suspicion as if I have committed some crime by expressing my solidarity with Palestinian people,” Nawaz, 20, told Al Jazeera. All this despite the Congress expressing its support for the “rights of the Palestinian people to land, self-government and to live with dignity” as the party called for an immediate ceasefire in a resolution passed by its working committee on October 9. Meanwhile, pro-Israel rallies, organised mainly byHindu right-wing groups, were seen across India, while many on social media offered their services to the Israeli forces. On Saturday, dozens of supporters of a retired Indian army soldier travelled 182km (113 miles) to reach the Israeli embassy in New Delhi where they offered to go to Israel to fight against the Palestinians in Gaza. Last week, one of India’s most influential Hindu nationalists, Yati Narsinghanand, released a video in which he said Hindus and Jews “have the same enemy: Muhammad and his satanic book” as he urged the Israeli government to allow 1,000 Hindus to settle in Israel in order to “take on those Muslims”. Israel’s ambassador to India, Naor Gilon, on October 8 said he had received several requests from Indians wanting to voluntarily fight for Israel. Apoorvanand, professor of Hindi language at Delhi University, told Al Jazeera he was not surprised that the Hindu far right, which openly admires Adolf Hitler for his action against the Jews, is now supporting the Zionists in Israel. “Hindu far-right organisations in India have always supported those who dominate by violence. Hitler did once, so they supported him. Now Israel is doing this, so they are supporting it,” he said. Apoorvanand said the Hindu right in India thinks there are ideological linkages between them and the Zionists in Israel. “It looks like Israel is fighting a proxy war on behalf of the Hindu far right. They think Israel is fighting and decimating Muslims on their behalf. The way they want to establish Akhand Bharat [Unified India] by joining Pakistan, Afghanistan, Nepal together with India, they think Israel is following the same expansionist ideology,” he said. This was not always the case. India’s foreign policy has historically supported the Palestinian cause, which began with India voting against the United Nations resolution to create the state of Israel in 1947 and then recognising the PLO as a representative of the Palestinian people in 1974. India’s pro-Palestine stand was guided by the shared history of colonisation by the British, Zikrur Rahman, former Indian ambassador to Palestine, told Al Jazeera. “In the postcolonial era, we identified that this is a colonial attempt to divide the country and to create another country. We were not in favour of the creation of a country on the basis of religion,” he said. Rahman, however, added that while India’s position on Palestine has not changed, it is not as strong as it used to be. India recognised the creation of Israel in 1950, but did not establish diplomatic relations until 1992, when the details of the firstOslo Accordwere being finalised. Since then, India has tried to strike a balance between its strategic relations with Israel and sympathising with the Palestinian struggle. Today, India is the largest buyer of Israeli-made weapons, while strategic and security cooperation between them has grown manifold. Comparisons have also been made between Israel demolishing homes of Palestinians in the occupied territories and a similar policy adopted by some BJP state governmentsmainly against Muslimsas forms of “collective punishment” of the community. Since Prime Minister Narendra Modi came to power in 2014, he has made public statements, calling his Israeli counterpart Benjamin Netanyahu a “good friend” on several occasions. Modi was one of the first global leaders to post his solidarity with Israel after Hamas’s unprecedented incursion on October 7. “Deeply shocked by the news of terrorist attacks in Israel,” said his post on X, which came four hours before US President Joe Biden reacted to the event. Modi also condemned the Israeli attack onal-Ahli Arab Hospitalin Gaza on October 18, in which nearly 500 Palestinians were killed, though his message on X appeared nearly eight hours after Biden’s post. Meanwhile, India’s Ministry of External Affairs issued a statement on October 12, reiterating New Delhi’s position of establishing a “sovereign, independent, and viable state of Palestine, living within secure and recognised borders, side by side at peace with Israel”. Last week, Modi posted on X about his phone call with the Palestinian Authority President Mahmoud Abbas, in which he repeated India’s “longstanding principled position on the Israel-Palestine issue”. He said his government is sending humanitarian assistance for the besieged residents of Gaza. Journalist Anand K Sahay, however, thinks India’s response to the unfolding humanitarian disaster in Gaza has not been adequate. “What India didn’t say is important. India didn’t demand a ceasefire. Historically, India has always demanded a ceasefire in case of a [foreign] war. In this case also we should have strongly said: stop the war,” he told Al Jazeera. Sahay said Modi’s flaunting of closeness with Israel is also aimed at appeasing his core vote bank: the Hindus. “Suppose there was another religion in majority in Palestine. Then our stand may have been different. During the Russia-Ukraine war, we said ‘this is not an age of war’. Why couldn’t we say this in case of Israel-Palestine war?” asked Sahay. “By not asking for a ceasefire, India was also indirectly signalling the US that the Indian position was very close to the US line.” Follow Al Jazeera English:	NaN	NaN
7	89555	NaN	The Indian Express	New York Times	No nation in the world is buying more planes than India. Here’s why.	India's largest airlines have ordered nearly 1,000 jets this year, committing tens of billions of dollars to a spending spree that is unparalleled in aviation.	https://indianexpress.com/article/business/aviation/no-nation-world-buying-more-planes-than-india-here-why-9010202/	https://images.indianexpress.com/2023/11/igiairport.jpg	2023-11-02 05:48:58.000000	No nation in the world is buying as many airplanes as India. Its largest airlines have ordered nearly 1,000 jets this year, committing tens of billions of dollars to a spending spree that is unparall… [+7083 chars]	Nepal	Written by Alex Travelli and Hari Kumar No nation in the world is buying as many airplanes as India. Its largest airlines have ordered nearly 1,000 jets this year, committing tens of billions of dollars to a spending spree that is unparalleled in aviation. In NewDelhi, Indira Gandhi International Airport will be ready for 109 million passengers next year, as it prepares to become the world’s second busiest, behind Hartsfield-Jackson Atlanta International Airport in the United States. And this is happening in a vast country still heavily reliant on trains — with 20 journeys by rail for every one by air. The enormous aviation build-out, with a surge of investment behind it, has pride of place in India’s case for a greater standing on the world stage. As it moves up the ranks of the world’s biggest economies, India is scrambling to meet the expanding ambitions of its ascendant middle class. Its airports present highly visible achievements. Air travel remains out of the financial reach of most Indians. An estimated 3% of the country’s population flies on a regular basis. But in a nation of 1.4 billion people, that percentage represents 42 million — executives, students and engineers who yearn to get quickly from here to there inside India’s borders, and to gain easier access to destinations beyond, for both business and vacation. Kapil Kaul, CEO of CAPA India, an advisory firm focused on aviation, calls “the next two to three years critical for achieving the quality of growth that India desires and deserves.” Growth has so far been profitless. Now Indian aviation must prove it can make money. The effects of the spending spree should redound across India’s economy. Cargo comes with passenger traffic, and foreign investment tends to follow closely behind, Kaul said. Arrivals to the international terminal at Indira Gandhi Airport are greeted by a wall of giant sculptural hands, their fingers and palms folded into the signifying shapes of the Buddha’s gestures, looking both ancient and futuristic. In 2012, when they were installed, 30 million passengers passed through the airport. By the time the airport has expanded to its new capacity, another one will have been built from scratch on the other side of the city. Indira Gandhi Airport is racing to get bigger. In July it added a fourth runway and opened an elevated taxiway. The company that operates it, GMR Airports, took over in 2006, a time when all arrivals walked past cows lazing in the dust to reach a taxi stand. By 2018 the facility was rated as India’s most valuable infrastructural asset. To spare the use of jet fuel, a battery-powered TaxiBot lugs idling planes around the tarmac. An automated luggage-handling system can sort 6,000 bags an hour. Two beneficiaries of India’s expanding aviation market are the world’s largest airplane makers: Boeing in America and Airbus in Europe. In February, Air India, which Tata Group took private last year, agreed to buy 250 planes from Airbus and 220 from Boeing, worth a combined $70 billion. In June, IndiGo, the country’s biggest carrier by passengers and flights, ordered 500 new Airbus A230s. The bulk of the growth of Indian aviation has been among homegrown airlines, which have clocked a 36% increase in passengers since 2022. Foreign tourist arrivals are rebounding since the pandemic, but are still relatively scarce, barely topping 10 million in a good year (about the same as Romania). So low-cost carriers are adding new countries to their destinations in order to accommodate India’s demand for foreign tourism. Azerbaijan, Kenya and Vietnam are all a direct flight from Delhi orMumbai, India’s financial capital, for less than 21,000 rupees ($250) one way. The air corridor between Delhi and Mumbai was already one of the world’s 10 busiest. Like Delhi, Mumbai has new airport terminals that would be the envy of any city in America, not to mention the glorious new all-bamboo Terminal 2 at Kempegowda International Airport in Bengaluru, a city in southern India. But the expansion in infrastructure is not limited to the country’s premier metropolitan areas. Prime MinisterNarendra Modi’s government likes to point out that the number of airports has doubled in the nine years since he took office, to 148 from 74. Jyotiraditya Scindia, Modi’s aviation minister, said there would be at least 230 by 2030. The government has invested more than $11 billion in airports over the past decade, and Scindia has promised another $15 billion. That means that sleepy towns such asDarbhanga, a former principality in the impoverished state of Bihar in eastern India, now have nonstop access to Delhi, Bengaluru and beyond. For many of the 900 travelers a day who fill its flights, including plenty from nearby Nepal, the new airport has transformed the journey. Prasanna Kumar Jha, 52, was born in Darbhanga but works in Delhi as a tax consultant. “Who ever expected that Darbhanga would be on the air map?” he asked. Flying to his hometown on short notice to see his ailing mother cost him 10,500 rupees ($126), which pinched. “But if you calculate the alternative — by train from Delhi and then taxi to Darbhanga — it will take at least 30 hours,” he said. “The plane journey is no longer a luxury but a necessity.” Darbhanga’s airport is a far cry from New Delhi’s. There is no parking lot. Passengers walk from the edge of a highway past a checkpoint to wait on benches outside the terminal. Then they wait on another set of outdoor benches after clearing the security check. But it works. Another passenger on the same flight at Darbhanga, Ajay Jha, was cradling his 1-year-old daughter, Saranya, as he stood near the rudimentary baggage claim. His family was on the last leg of a trip that started in Bellevue, Washington, where he works as an engineer for Amazon, to a family reunion in the Bihari countryside. Traveling halfway around the world took less time than Jha used to spend getting home from his school in Bengaluru. Yet a vast majority of Indians cannot afford such conveniences. The annual mean income is still less than a single economy-class fare from the United States, and, in this top-heavy economy, most Indians earn much less than that. Middle class, in Indian parlance, indicates somewhere close to the top of the pyramid. A report by CAPA India counted just 0.13 passenger seats per capita in 2019 for Indians, compared with 0.52 for Chinese and 3.03 for Americans. But aviation companies and India’s elected officials look at the low penetration and see opportunity. A scarcity of competition, in the face of an emerging duopoly between IndiGo and the Tata-led airlines, is one of the new landscape’s most striking features. Smaller competitors keep going bust, most recently Go First, which declared bankruptcy in May. A shortage of pilots, after dozens were poached by bigger companies, forced Akasa Air, a promising upstart, to cancel flights in August. But supply shortages are not the worst kind of problem to have in today’s global economy. With aviation’s growth in the decade before the pandemic steady at about 15% a year, the Indian boom seems all but guaranteed to change the future of aviation worldwide. If the benefits accruing to the winners in India’s economy can be coaxed into trickling outward and downward, the same could go for many other sectors.	NaN	NaN
8	89556	abc-news-au	ABC News (AU)	ABC News	More than 130 people have reportedly been taken to Gaza as hostages. Here's what we know	Hamas fighters have escaped back to Gaza with dozens of captives, including women, children and the elderly, after an unprecedented attack that has seen at least 700 people killed in Israel, followed by more than 400 in the Palestinian territories after retal…	https://www.abc.net.au/news/2023-10-09/hamas-takes-hostages-to-gaza/102950956	https://live-production.wcms.abc-cdn.net.au/ec96e8c1e1aca987e5156a05e3429b91?impolicy=wcms_crop_resize&cropH=450&cropW=800&xPos=0&yPos=75&width=862&height=485	2023-10-09 01:09:03.000000	Hamas fighters have escaped back to Gaza with dozens of captives following the group's unprecedented attack on Israel which saw at least 700 Israelis killed, followed by 400 Palestinians after retali… [+1553 chars]	Nepal	NaN	NaN	NaN
9	89837	bbc-news	BBC News	NaN	World Cup: New Zealand v Bangladesh - clips, radio & text	Follow live text, in-play video clips and radio commentary as New Zealand play Bangladesh in the Men's Cricket World Cup 2023.	https://www.bbc.co.uk/sport/live/cricket/66854573	https:////m.files.bbci.co.uk/modules/bbc-morph-sport-seo-meta/1.23.3/images/bbc-sport-logo.png	2023-10-12 14:27:52.000000	Here we are then. Match 11 of the Cricket World Cup. A week and a day into the tournament and we've seen records broken, plenty of passion and a decent helping of DRS controversy.\nWhat we haven't ha… [+252 chars]	New Zealand	NaN	NaN	NaN

	article_id	source_id	source_name	author	title	description	url	url_to_image	published_at	content	category	full_content	article	title_sentiment
32623	626996	NaN	XDA Developers	Karthik Iyer	Five ways Microsoft can improve the Xbox Game Bar on PC	Here are a few ways Microsoft can improve the Xbox Game Bar on PC to improve the overall gaming experience.	https://www.xda-developers.com/five-ways-microsoft-improve-xbox-game-bar/	https://static1.xdaimages.com/wordpress/wp-content/uploads/wm/2023/11/xbox-game-bar-on-a-windows-laptop.jpg	2023-11-22 11:00:21.000000	Key Takeaways\r\n<ul><li> The Xbox Game Bar needs to add advanced recording options, including support for 4K recording and higher frame rates, to keep up with modern gaming technology. </li><li> The l… [+7974 chars]	Games	NaN	NaN	NaN
32624	626997	espn	ESPN	Jenna Laine	Bucs hoping 'small things' don't hold them back from winning, playoffs	Despite being 4-6, the Bucs are still only one game behind the Saints in the NFC South with key divisional games looming down the stretch.	https://www.espn.com/nfl/story/_/id/38952788/bucs-hope-small-things-hold-back-playoffs	https://a2.espncdn.com/combiner/i?img=%2Fphoto%2F2023%2F1121%2Fr1256196_1296x729_16%2D9.jpg	2023-11-22 11:00:10.000000	TAMPA, Fla. -- Despite a 27-14 loss at the San Francisco 49ers on Sunday, the Tampa Bay Buccaneers, at 4-6, are still hopeful.Coach Todd Bowles emphasized after the Week 11 loss at Levi's Stadium tha… [+4858 chars]	Games	NaN	NaN	NaN
32625	626998	NaN	La Vanguardia	Silvia Fernández	Google estuvo cerca de comprar Epic Games antes de lanzar Stadia	Hace unos meses, concretamente el 18 de enero de este año, Google decidió cerrar definitivamente su servicio para videojuegos en la nube: Stadia. Antes de desactivar sus servidores, el gigante tecnológico quiso dar una última alegría a sus usuarios, así que a…	https://www.lavanguardia.com/andro4all/google/google-cerca-comprar-epic-games-antes-lanzar-stadia	https://www.lavanguardia.com/andro4all/hero/2023/11/google-epic-games-ok.png?width=1200	2023-11-22 11:19:34.000000	La idea de Google era que Fortnite se convirtiera en una pieza clave en Android\r\nHace unos meses, concretamente el 18 de enero de este año, Google decidió cerrar definitivamente su servicio para vide… [+3070 chars]	Games	NaN	NaN	NaN
32626	626999	techradar	TechRadar	Rhys Wood	GameMaker engine transforms its business model and introduces a one-time fee	YoYo Games has changed its business model for the GameMaker engine, making it much more accessible.	https://www.techradar.com/gaming/consoles-pc/gamemaker-engine-transforms-its-business-model-and-introduces-a-one-time-fee	https://cdn.mos.cms.futurecdn.net/AC4kemFeGFmGqXhKyeGEFP-1200-80.png	2023-11-22 10:13:44.000000	YoYo Games, creator of the popular GameMaker engine, has switched up its pricing model and appears to be much more accessible as a result.\r\nAn official blog post written by GameMaker head Russell Kay… [+1643 chars]	Games	NaN	NaN	NaN
32627	627000	ign	IGN	Luke Reilly	Bluey: The Videogame Review	Bluey: The Videogame may look the part but, with shonky controls and barely two hours of gameplay, everywhere else it’s a dog’s breakfast.	https://www.ign.com/articles/bluey-the-videogame-review	https://assets-prd.ignimgs.com/2023/11/22/bluey-thumb-1700628294058.png?width=1280	2023-11-22 08:14:17.000000	The very same weekend we received Bluey: The Videogame, Australias ABC broadcasted a countdown of the top 100 Bluey episodes, as voted by the public. Tuning in for the final 20-or-so, it was the perf… [+5943 chars]	Games	NaN	NaN	NaN
32628	627001	NaN	Ascii.jp	ASCII	なんと1年ぶり！『フォートナイト』リアルタイムイベント「BIG BANG」を12月3日4時に開催決定	2023年11月22日、Epic Gamesはバトルロイヤルゲーム『Fortnite』（フォートナイト）にて、12月3日4時（日本時間）より、リアルタイムイベント「BIG BANG（ビッグバン）」を開催すると発表した。	https://weekly.ascii.jp/elem/000/004/170/4170864/	https://ascii.jp/img/2023/11/22/3643518/l/56d5fc71b95cf6e8.jpg	2023-11-22 05:00:00.000000	Epic Games1122Fortnite20231234\r\nhttps://www.fortnite.com/news/the-big-bang-event-a-new-beginning-for-fortnite-on-december-2\r\n1\r\n3 1\r\n4 1https://ascii.jp/elem/000/004/116/4116037/\r\n20231121\r\n1\r\n48……\r\n… [+316 chars]	Games	NaN	NaN	NaN
32629	627002	NaN	Rock Paper Shotgun	Edwin Evans-Thirlwell	The grandest factory sim ever made is getting an interplanetary combat system this December	Dyson Sphere Program is already rather overwhelming - this is, after all, a strategy sim about wrapping whole planets in conveyor belts so as to build cages around stars and harvest their juices - and now the absolute madfolk of Youthcat Studio are adding com…	https://www.rockpapershotgun.com/the-grandest-factory-sim-ever-made-is-getting-an-interplanetary-combat-system-this-december	https://assetsio.reedpopcdn.com/1_79VFemo.jpg?width=1200&height=630&fit=crop&enable=upscale&auto=webp	2023-11-22 10:38:48.000000	Dyson Sphere Program is already rather overwhelming - this is, after all, a strategy sim about wrapping whole planets in conveyor belts so as to build cages around stars and harvest their juices - an… [+2648 chars]	Games	NaN	NaN	NaN
32630	627003	NaN	SamMobile	Sagar Naresh	Google tried to buy a controlling stake in Epic Games before Stadia launched	Some interesting titbits have popped up in the ongoing Epic Games vs Google trial. According to the latest emails that have popped up, Google tried to buy out Epic Games even before Stadia, the dead cloud-gaming platform, was launched. Google thought of this …	https://www.sammobile.com/news/google-tried-to-buy-a-controlling-stake-in-epic-games-before-stadia-launched/	https://www.sammobile.com/wp-content/uploads/2022/09/Google-Stadia-720x480.jpg	2023-11-22 05:06:35.000000	Some interesting titbits have popped up in the ongoing Epic Games vs Google trial. According to the latest emails that have popped up, Google tried to buy out Epic Games even before Stadia, the dead … [+1836 chars]	Games	NaN	NaN	NaN
32631	627004	NaN	MIT Technology Review	Zeyi Yang	This Chinese map app wants to be a super app for everything outdoors	This story first appeared in China Report, MIT Technology Review’s newsletter about technology in China. Sign up to receive it in your inbox every Tuesday. Thanksgiving is almost here. This year, when you get together with your family, may I suggest a fun lit…	https://www.technologyreview.com/2023/11/22/1083805/chinese-map-super-app-game/	https://wp.technologyreview.com/wp-content/uploads/2023/11/super-map.jpg?resize=1200,600	2023-11-22 11:00:00.000000	Amap, for example, is one of the most widely used map and navigation apps in China today. But when I open it on my phone, I can see over 30 functions that you wouldnt find on Western-equivalent apps.… [+3234 chars]	Games	NaN	NaN	NaN
32632	627005	NaN	PC Gamer	Harvey Randall	An Armored Core 6 co-op mod has a fully-functional alpha, though its creator wants to add a few key features like difficulty scaling first	LukeYui's been adding multiplayer functionality to FromSoftware games for a while—and Armored Core 6 is next in line.	https://www.pcgamer.com/an-armored-core-6-co-op-mod-has-a-fully-functional-alpha-though-its-creator-wants-to-add-a-few-key-features-like-difficulty-scaling-first/	https://cdn.mos.cms.futurecdn.net/u6BEypjgPabDw	NaN	NaN	NaN	NaN	NaN	NaN

Most frequently occurring

	source_id	source_name	author	title	description	url	url_to_image	published_at	content	category	full_content	article	title_sentiment	# duplicates
132	NaN	[Removed]	NaN	[Removed]	[Removed]	https://removed.com	NaN	1970-01-01 00:00:00.000000	[Removed]	Portugal	NaN	NaN	NaN	56
98	NaN	[Removed]	NaN	[Removed]	[Removed]	https://removed.com	NaN	1970-01-01 00:00:00.000000	[Removed]	Food	NaN	NaN	NaN	43
106	NaN	[Removed]	NaN	[Removed]	[Removed]	https://removed.com	NaN	1970-01-01 00:00:00.000000	[Removed]	Home	NaN	NaN	NaN	41
80	NaN	[Removed]	NaN	[Removed]	[Removed]	https://removed.com	NaN	1970-01-01 00:00:00.000000	[Removed]	Australia	NaN	NaN	NaN	39
119	NaN	[Removed]	NaN	[Removed]	[Removed]	https://removed.com	NaN	1970-01-01 00:00:00.000000	[Removed]	Love	NaN	NaN	NaN	23
85	NaN	[Removed]	NaN	[Removed]	[Removed]	https://removed.com	NaN	1970-01-01 00:00:00.000000	[Removed]	COVID	NaN	NaN	NaN	19
151	NaN	[Removed]	NaN	[Removed]	[Removed]	https://removed.com	NaN	1970-01-01 00:00:00.000000	[Removed]	Travel	NaN	NaN	NaN	19
89	NaN	[Removed]	NaN	[Removed]	[Removed]	https://removed.com	NaN	1970-01-01 00:00:00.000000	[Removed]	China	NaN	NaN	NaN	18
113	NaN	[Removed]	NaN	[Removed]	[Removed]	https://removed.com	NaN	1970-01-01 00:00:00.000000	[Removed]	Israel	NaN	NaN	NaN	18
153	NaN	[Removed]	NaN	[Removed]	[Removed]	https://removed.com	NaN	1970-01-01 00:00:00.000000	[Removed]	Ukraine	NaN	NaN	NaN	18

Overview

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Latin

Common

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ASCII

Most occurring characters

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Lowercase Letter

Uppercase Letter

Decimal Number

Other Punctuation

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Math Symbol

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Most occurring scripts

Most frequent character per script

Latin

Common

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ASCII

None

Punctuation

Most occurring characters

Most occurring categories

Most frequent character per category

Other Letter

Lowercase Letter

Uppercase Letter

Other Punctuation

Decimal Number

Spacing Mark

Nonspacing Mark

Math Symbol

Other Symbol

Space Separator

Open Punctuation

Close Punctuation

Dash Punctuation

Final Punctuation

Format

Control

Connector Punctuation

Modifier Letter

Initial Punctuation

Most occurring scripts

Most frequent character per script

Han

Latin

Cyrillic

Katakana

Common

Greek

Devanagari

Hiragana

Arabic

Hangul

Malayalam

Hebrew

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